Biomarkers for Fatty Liver Disease and Methods Using the Same

ABSTRACT

The present invention provides various biomarkers of fatty liver disease, including steatosis and steatohepatitis. The present invention also provides various methods of using the biomarkers, including methods for diagnosis of fatty liver disease, methods of determining predisposition to fatty liver disease, methods of monitoring progression/regression of fatty liver disease, methods of assessing efficacy of compositions for treating fatty liver disease, methods of screening compositions for activity in modulating biomarkers of fatty liver disease, methods of treating fatty liver disease, as well as other methods based on biomarkers of fatty liver disease.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No. 12/771,729, filed Apr. 30, 2010, which is a continuation-in-part of PCT Application Serial No. PCT/US2008/082013, filed Oct. 31, 2008, designating the United States and published in English, which claims the benefit of the filing date under 35 U.S.C. § 119(e) of Provisional U.S. Patent Application No. 60/984,942, filed Nov. 2, 2007, and Provisional U.S. Patent Application No. 61/042,459, filed Apr. 4, 2008. All of the foregoing applications are hereby incorporated by reference.

FIELD

The invention generally relates to biomarkers for fatty liver disease and methods based on the same biomarkers.

BACKGROUND

Fatty change in the liver results from excessive accumulation of lipids within hepatocytes. Fatty liver is the accumulation of triglycerides and other fats in the liver cells. Fatty liver disease can range from fatty liver alone (simple fatty liver, steatosis) to fatty liver associated with hepatic inflammation (steatohepatitis). Although having fat in the liver is not normal, by itself it probably causes little harm or permanent damage. Steatosis is generally believed to be a benign condition, with rare progression to chronic liver disease. In contrast, steatohepatitis may progress to liver fibrosis and cirrhosis, can be associated with hepatocellular carcinoma and may result in liver-related morbidity and mortality.

Steatosis can occur with the use of alcohol (alcohol-related fatty liver) or in the absence of alcohol. Steatohepatitis may be related to alcohol-induced hepatic damage or may be unrelated to alcohol. If steatohepatitis is present but a history of alcohol use is not, the condition is termed nonalcoholic steatohepatitis (NASH).

In the absence of alcohol the main risk factors for simple fatty liver and NASH are obesity, diabetes, and high triglyceride levels. In NASH, fat builds up in the liver and eventually causes scar tissue. This type of hepatitis appears to be associated with diabetes, protein malnutrition, obesity, coronary artery disease, and treatment with corticosteroid medications. Fibrosis or cirrhosis in the liver is present in 15-50% of patients with NASH. Approximately 30% of patients with fibrosis develop cirrhosis after 10 years.

Fatty liver disease is now the most common cause for elevated liver function tests in the United States. It is now probably the leading reason for mild elevations of transaminases. Steatosis affects approximately 25-35% of the general population. Non-alcoholic fatty liver disease (including steatosis and steatohepatitis) (NAFLD) is found in over 80% of patients who are obese. NASH affects 2 to 5 percent of Americans and has been detected in 1.2-9% of patients undergoing routine liver biopsy. Over 50% of patients undergoing bariatric surgery have NASH. The disease strikes males and females; early studies report >70% of cases were in females but recent studies report 50% of patients are females. Fatty liver occurs in all age groups. In the United States NASH is the most common liver disease among adolescents and is the third most common cause of chronic liver disease in adults (after hepatitis C and alcohol).

Both NASH and NAFLD are becoming more common, possibly because of the greater number of Americans with obesity. In the past 10 years, the rate of obesity has doubled in adults and tripled in children. Obesity also contributes to diabetes and high blood cholesterol, which can further complicate the health of someone with NASH. Diabetes and high blood cholesterol are also becoming more common among Americans.

NASH is usually a silent disease with few or no symptoms. Patients generally feel well in the early stages and only begin to have symptoms-such as fatigue, weight loss, and weakness-once the disease is more advanced or cirrhosis develops. The progression of NASH can take years, even decades. The process can stop and, in some cases, reverse on its own without specific therapy. Or NASH can slowly worsen, causing scarring or “fibrosis” to appear and accumulate in the liver. As fibrosis worsens, cirrhosis develops; the liver becomes seriously scarred, hardened, and unable to function normally. Not every person with NASH develops cirrhosis, but once serious scarring or cirrhosis is present, few treatments can halt the progression. A person with cirrhosis experiences fluid retention, muscle wasting, bleeding from the intestines, and liver failure. Liver transplantation is the only treatment for advanced cirrhosis with liver failure, and transplantation is increasingly performed in people with NASH. NASH ranks as one of the major causes of cirrhosis in America, behind hepatitis C and alcoholic liver disease.

NASH is usually first suspected in a person who is found to have elevations in liver tests that are included in routine blood test panels, such as alanine aminotransferase (ALT) or aspartate aminotransferase (AST). When further evaluation shows no apparent reason for liver disease (such as medications, viral hepatitis, or excessive use of alcohol) and when x-rays or imaging studies of the liver show fat, NASH is suspected. The only means of proving a diagnosis of NASH and separating it from simple fatty liver is a liver biopsy. A liver biopsy requires a needle to be inserted through the skin and the removal of a small piece of the liver. If the tissue shows fat without inflammation and damage, simple fatty liver is diagnosed. NASH is diagnosed when microscopic examination of the tissue shows fat along with inflammation and damage to liver cells. A biopsy is required to determine whether scar tissue has developed in the liver. Currently, no blood tests or scans can reliably provide this information. Therefore there exists a need for a less invasive diagnostic method (i.e. a method that would not require a biopsy).

SUMMARY

The present invention provides methods of diagnosing whether a subject has steatohepatitis, comprising: analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers for steatohepatitis in the sample, where the one or more biomarkers are selected from Tables 1, 3, 4B, 5B, 6B, 9, 10, 11, 12, 13, and 15 and FIGS. 2, 3, 12, and 13 and comparing the level(s) of the one or more biomarkers in the sample to steatohepatitis-positive and/or steatohepatitis-negative reference levels of the one or more biomarkers in order to diagnose whether the subject has steatohepatitis.

The present invention also provides methods of diagnosing whether a subject has steatosis, comprising: analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers for steatosis in the sample, where the one or more biomarkers are selected from Tables 2, 3, 4B, 5B, 6B, 8, 9, 11, 12, 13, and 15 and FIGS. 1, 3, and 12; and comparing the level(s) of the one or more biomarkers in the sample to steatosis-positive and/or steatosis-negative reference levels of the one or more biomarkers in order to diagnose whether the subject has steatosis.

Also provided are methods of determining whether a subject is predisposed to developing steatohepatitis, comprising: analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers for steatohepatitis in the sample, where the one or more biomarkers are selected from Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, and 15 and FIGS. 2, 3, 12 and 13; and comparing the level(s) of the one or more biomarkers in the sample to steatohepatitis-positive and/or steatohepatitis-negative reference levels of the one or more biomarkers in order to determine whether the subject is predisposed to developing steatohepatitis.

The present invention further provides methods of determining whether a subject is predisposed to developing steatosis, comprising: analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers for steatosis in the sample, where the one or more biomarkers are selected from Tables 2, 3, 4B, 5B, 8, 9, 11, 12, 13, and 15 and FIGS. 1, 3, and 12; and comparing the level(s) of the one or more biomarkers in the sample to steatosis-positive and/or steatosis-negative reference levels of the one or more biomarkers in order to determine whether the subject is predisposed to developing steatosis.

In addition, the present invention provides methods of monitoring progression/regression of steatohepatitis in a subject comprising: analyzing a first biological sample from a subject to determine the level(s) of one or more biomarkers for steatohepatitis in the sample, where the one or more biomarkers are selected from Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, and 15 and FIGS. 2, 3, 12, and 13 and the first sample is obtained from the subject at a first time point; analyzing a second biological sample from a subject to determine the level(s) of the one or more biomarkers, where the second sample is obtained from the subject at a second time point; and comparing the level(s) of one or more biomarkers in the first sample to the level(s) of the one or more biomarkers in the second sample in order to monitor the progression/regression of steatohepatitis in the subject.

The present invention also provides methods of monitoring progression/regression of steatosis in a subject comprising: analyzing a first biological sample from a subject to determine the level(s) of one or more biomarkers for steatosis in the sample, where the one or more biomarkers are selected from Tables 2, 3, 4B, 5B, 8, 9, 11, 12, 13, and 15 and FIGS. 1, 3, and 12 and the first sample is obtained from the subject at a first time point; analyzing a second biological sample from a subject to determine the level(s) of the one or more biomarkers, wherein the second sample is obtained from the subject at a second time point; and comparing the level(s) of one or more biomarkers in the first sample to the level(s) of the one or more biomarkers in the second sample in order to monitor the progression/regression of steatosis in the subject.

Further provided are methods of assessing the efficacy of a composition for treating steatohepatitis comprising: analyzing, from a subject having steatohepatitis and currently or previously being treated with a composition, a biological sample to determine the level(s) of one or more biomarkers for steatohepatitis selected from Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, and 15 and FIGS. 2, 3, 12, and 13; and comparing the level(s) of the one or more biomarkers in the sample to (a) levels of the one or more biomarkers in a previously-taken biological sample from the subject, where the previously-taken biological sample was obtained from the subject before being treated with the composition, (b) steatohepatitis-positive reference levels of the one or more biomarkers, and/or (c) steatohepatitis-negative reference levels of the one or more biomarkers.

The present invention further provides methods for assessing the efficacy of a composition in treating steatohepatitis, comprising: analyzing a first biological sample from a subject to determine the level(s) of one or more biomarkers for steatohepatitis selected from Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, and 15 and FIGS. 2, 3, 12, and 13, the first sample obtained from the subject at a first time point; administering the composition to the subject; analyzing a second biological sample from the subject to determine the level(s) of the one or more biomarkers, the second sample obtained from the subject at a second time point after administration of the composition; and comparing the level(s) of one or more biomarkers in the first sample to the level(s) of the one or more biomarkers in the second sample in order to assess the efficacy of the composition for treating steatohepatitis.

The present invention also provides methods of assessing the relative efficacy of two or more compositions for treating steatohepatitis comprising: analyzing, from a first subject having steatohepatitis and currently or previously being treated with a first composition, a first biological sample to determine the level(s) of one or more biomarkers selected from Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, and 15 and FIGS. 2, 3, 12, and 13; analyzing, from a second subject having steatohepatitis and currently or previously being treated with a second composition, a second biological sample to determine the level(s) of the one or more biomarkers; and comparing the level(s) of one or more biomarkers in the first sample to the level(s) of the one or more biomarkers in the second sample in order to assess the relative efficacy of the first and second compositions for treating steatohepatitis.

Further, the present invention provides methods for screening a composition for activity in modulating one or more biomarkers of steatohepatitis, comprising: contacting one or more cells with a composition; analyzing at least a portion of the one or more cells or a biological sample associated with the cells to determine the level(s) of one or more biomarkers of steatohepatitis selected from Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, and 15 and FIGS. 2, 3, 12, and 13; and comparing the level(s) of the one or more biomarkers with predetermined standard levels for the biomarkers to determine whether the composition modulated the level(s) of the one or more biomarkers.

The present invention further provides methods for identifying a potential drug target for steatohepatitis comprising: identifying one or more biochemical pathways associated with one or more biomarkers for steatohepatitis selected from Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, and 15 and FIGS. 2, 3, 12, and 13; and identifying a protein affecting at least one of the one or more identified biochemical pathways, the protein being a potential drug target for steatohepatitis.

The present invention also provides methods for treating a subject having steatohepatitis comprising administering to the subject an effective amount of one or more biomarkers selected from Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, and 15 and FIGS. 2, 3, 12 and 13 that are decreased in steatohepatitis.

The present invention also provides methods of distinguishing steatosis from NASH in a subject having steatosis, comprising: analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers for steatosis and/or NASH in the sample, where the one or more biomarkers are selected from Tables 1, 2, 3, 4B, 5B, 6B, 8, 9, 10, 11, 12, 13, and 15 and FIGS. 1, 2, 3, 12, and 13; and comparing the level(s) of the one or more biomarkers in the sample to steatosis-positive reference levels that distinguish over NASH and/or to NASH-positive reference levels that distinguish over steatosis in order to determine whether the subject has NASH.

In addition, the present invention provides methods of diagnosing whether a subject has NASH, comprising: analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers for NASH in the sample, where the one or more biomarkers are selected from Tables 1, 2, 3, 4B, 5B, 6B, 8, 9, 10, 11, 12, 13, and 15 and FIGS. 1, 2, 3, 12, and 13 and comparing the level(s) of the one or more biomarkers in the sample to NASH-positive and/or NASH-negative reference levels of the one or more biomarkers in order to diagnose whether the subject has NASH.

The present invention also provides methods of assessing the efficacy of a composition for treating steatosis comprising: analyzing, from a subject having steatosis and currently or previously being treated with a composition, a biological sample to determine the level(s) of one or more biomarkers for steatosis selected from Tables 2, 3, 4B, 5B, 8, 9, 11, 12, 13, and 15 and FIGS. 1, 3, and 12; and comparing the level(s) of the one or more biomarkers in the sample to (a) levels of the one or more biomarkers in a previously-taken biological sample from the subject, where the previously-taken biological sample was obtained from the subject before being treated with the composition, (b) steatosis-positive reference levels of the one or more biomarkers, and/or (c) steatosis-negative reference levels of the one or more biomarkers.

Also provided are methods of assessing the relative efficacy of two or more compositions for treating steatosis comprising: analyzing, from a first subject having steatosis and currently or previously being treated with a first composition, a first biological sample to determine the level(s) of one or more biomarkers selected from Tables 2, 3, 4B, 5B, 8, 9, 11, 12, 13, and 15 and FIGS. 1, 3, and 12; analyzing, from a second subject having steatosis and currently or previously being treated with a second composition, a second biological sample to determine the level(s) of the one or more biomarkers; and comparing the level(s) of one or more biomarkers in the first sample to the level(s) of the one or more biomarkers in the second sample in order to assess the relative efficacy of the first and second compositions for treating steatosis.

The present invention also provides methods for screening a composition for activity in modulating one or more biomarkers of steatosis, comprising: contacting one or more cells with a composition; analyzing at least a portion of the one or more cells or a biological sample associated with the cells to determine the level(s) of one or more biomarkers of steatosis selected from Tables 2, 3, 4B, 5B, 8, 9, 11, 12, 13, and 15 and FIGS. 1, 3, and 12; and comparing the level(s) of the one or more biomarkers with predetermined standard levels for the biomarkers to determine whether the composition modulated the level(s) of the one or more biomarkers.

Also provided are methods for identifying a potential drug target for steatosis comprising: identifying one or more biochemical pathways associated with one or more biomarkers for steatosis selected from Tables 2, 3, 4B, 5B, 8, 9, 11, 12, 13, and 15 and FIGS. 1, 3, and 12; and identifying a protein affecting at least one of the one or more identified biochemical pathways, the protein being a potential drug target for steatosis.

The present invention also provides methods for treating a subject having steatosis comprising administering to the subject an effective amount of one or more biomarkers selected from Tables 2, 3, 4B, 5B, 8, 9, 11, 12, 13, and 15 and FIGS. 1, 3, and 12 that are decreased in steatosis.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 provides one embodiment of biomarker compounds that are important in distinguishing steatosis subjects and Control subjects.

FIG. 2 provides one embodiment of biomarker compounds that are important in distinguishing steatohepatitis subjects from control subjects.

FIG. 3 provides an embodiment of biomarker compounds that are important in distinguishing steatosis, steatohepatitis and control subjects.

FIG. 4 provides one example of recursive partitioning results showing the classification of Control and steatosis subjects using the biomarkers identified in Table 8.

FIG. 5 provides an example of recursive partitioning results showing the classification of steatosis and steatohepatitis (NASH, SH) using the biomarkers listed in Table 9.

FIG. 6 provides an example of recursive partitioning results showing the classification of control and steatohepatitis using the biomarkers listed in Table 10.

FIG. 7 provides an example of recursive partitioning results showing the use of the biomarkers to classify steatosis, steatohepatitis and control subjects using the biomarkers listed in Table 11.

FIG. 8 provides box plots of plasma levels of bile salts in healthy controls and subjects with steatosis and NASH as described in Example 6. Median scaled values are presented on the y-axis. Only bile salts that were significantly different (P<0.05) between controls and NASH are shown.

FIG. 9 provides box plots of plasma levels of glutamyl amino acids in healthy controls and subjects with steatosis and NASH as described in Example 6. All are significantly different (P<0.05) in NASH and steatosis compared with controls except γ-glutamylleucine, which is significantly higher in NASH only. Glutathione metabolism is up-regulated in subjects with NAFLD.

FIG. 10 provides box plots of plasma concentration of carnitine and acylcarnitines in subjects with NAFLD and healthy controls as described in Example 6. In FIGS. 10A and 10D, for carnitine and butyrylcarnitine, respectively, P<0.05 for NASH vs controls and for steatosis vs controls. In FIGS. 10B and 10C, for propionylcarnitine and 2-methylbutyrylcarnitine, respectively, P<0.05 for NASH vs controls, but not significant for steatosis vs controls.

FIG. 11 provides box plots of plasma concentration of branched-chain amino acids, tyrosine, and glutamate in healthy controls and subjects with steatosis and NASH as described in Example 6. For NASH vs controls, P<0.05 for all. For steatosis vs controls, P<0.05 for glutamate, tyrosine, and isoleucine.

FIG. 12 illustrates a random forest importance plot for all subjects as described in Example 6.

FIG. 13 illustrates a random forest importance plot for controls vs NASH as described in Example 6.

DETAILED DESCRIPTION

The present invention relates to biomarkers of steatosis and steatohepatitis, methods for diagnosis (or assisting in the diagnosis) of steatosis and/or steatohepatitis, methods of distinguishing between hepatic steatosis and steatohepatitis, methods of distinguishing between steatosis and NASH, methods of determining predisposition to steatohepatitis, fibrosis and cirrhosis, methods of monitoring progression/regression of steatohepatitis, methods of assessing efficacy of compositions for treating steatohepatitis, methods of screening compositions for activity in modulating biomarkers of steatohepatitis, methods of treating steatohepatitis, as well as other methods based on biomarkers of steatosis and steatohepatitis. Prior to describing this invention in further detail, however, the following terms will first be defined.

Definitions

“Biomarker” means a compound, preferably a metabolite, that is differentially present (i.e., increased or decreased) in a biological sample from a subject or a group of subjects having a first phenotype (e.g., having a disease) as compared to a biological sample from a subject or group of subjects having a second phenotype (e.g., not having the disease). A biomarker may be differentially present at any level, but is generally present at a level that is increased by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%, by at least 80%, by at least 85%, by at least 90%, by at least 95%, by at least 100%, by at least 110%, by at least 120%, by at least 130%, by at least 140%, by at least 150%, or more; or is generally present at a level that is decreased by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%, by at least 80%, by at least 85%, by at least 90%, by at least 95%, or by 100% (i.e., absent). A biomarker is preferably differentially present at a level that is statistically significant (i.e., a p-value less than 0.05 and/or a q-value of less than 0.10 as determined using either Welch's T-test or Wilcoxon's rank-sum Test).

The “level” of one or more biomarkers means the absolute or relative amount or concentration of the biomarker in the sample.

“Sample” or “biological sample” means biological material isolated from a subject. The biological sample may contain any biological material suitable for detecting the desired biomarkers, and may comprise cellular and/or non-cellular material from the subject. The sample can be isolated from any suitable biological tissue or fluid such as, for example, tissue, blood, blood plasma, urine, or cerebral spinal fluid (CSF).

“Subject” means any animal, but is preferably a mammal, such as, for example, a human, monkey, non-human primate, mouse, or rabbit.

A “reference level” of a biomarker means a level of the biomarker that is indicative of a particular disease state, phenotype, or predisposition to developing a particular disease state or phenotype, or lack thereof, as well as combinations of disease states, phenotypes, or predisposition to developing a particular disease state or phenotype, or lack thereof. A “positive” reference level of a biomarker means a level that is indicative of a particular disease state or phenotype. A “negative” reference level of a biomarker means a level that is indicative of a lack of a particular disease state or phenotype. For example, a “NASH-positive reference level” of a biomarker means a level of a biomarker that is indicative of a positive diagnosis of NASH in a subject, and a “NASH-negative reference level” of a biomarker means a level of a biomarker that is indicative of a negative diagnosis of NASH in a subject. A “reference level” of a biomarker may be an absolute or relative amount or concentration of the biomarker, a presence or absence of the biomarker, a range of amount or concentration of the biomarker, a minimum and/or maximum amount or concentration of the biomarker, a mean amount or concentration of the biomarker, and/or a median amount or concentration of the biomarker; and, in addition, “reference levels” of combinations of biomarkers may also be ratios of absolute or relative amounts or concentrations of two or more biomarkers with respect to each other. Appropriate positive and negative reference levels of biomarkers for a particular disease state, phenotype, or lack thereof may be determined by measuring levels of desired biomarkers in one or more appropriate subjects, and such reference levels may be tailored to specific populations of subjects (e.g., a reference level may be age-matched or gender-matched so that comparisons may be made between biomarker levels in samples from subjects of a certain age or gender and reference levels for a particular disease state, phenotype, or lack thereof in a certain age or gender group). Such reference levels may also be tailored to specific techniques that are used to measure levels of biomarkers in biological samples (e.g., LC-MS, GC-MS, etc.), where the levels of biomarkers may differ based on the specific technique that is used.

“Non-biomarker compound” means a compound that is not differentially present in a biological sample from a subject or a group of subjects having a first phenotype (e.g., having a first disease) as compared to a biological sample from a subject or group of subjects having a second phenotype (e.g., not having the first disease). Such non-biomarker compounds may, however, be biomarkers in a biological sample from a subject or a group of subjects having a third phenotype (e.g., having a second disease) as compared to the first phenotype (e.g., having the first disease) or the second phenotype (e.g., not having the first disease).

“Metabolite”, or “small molecule”, means organic and inorganic molecules which are present in a cell. The term does not include large macromolecules, such as large proteins (e.g., proteins with molecular weights over 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, or 10,000), large nucleic acids (e.g., nucleic acids with molecular weights of over 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, or 10,000), or large polysaccharides (e.g., polysaccharides with a molecular weights of over 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, or 10,000). The small molecules of the cell are generally found free in solution in the cytoplasm or in other organelles, such as the mitochondria, where they form a pool of intermediates which can be metabolized further or used to generate large molecules, called macromolecules. The term “small molecules” includes signaling molecules and intermediates in the chemical reactions that transform energy derived from food into usable forms. Examples of small molecules include sugars, fatty acids, amino acids, nucleotides, intermediates formed during cellular processes, and other small molecules found within the cell.

“Metabolic profile”, or “small molecule profile”, means a complete or partial inventory of small molecules within a targeted cell, tissue, organ, organism, or fraction thereof (e.g., cellular compartment). The inventory may include the quantity and/or type of small molecules present. The “small molecule profile” may be determined using a single technique or multiple different techniques.

“Metabolome” means all of the small molecules present in a given organism.

“Steatosis” refers to fatty liver disease without the presence of inflammation. The condition can occur with the use of alcohol or in the absence of alcohol use.

“Steatohepatitis” refers to fatty liver disease that is associated with inflammation. Steatohepatitis can progress to cirrhosis and can be associated with hepatocellular carcinoma. The condition can occur with the use of alcohol or in the absence of alcohol use.

“Non-alcoholic fatty liver disease” (NAFLD) refers to fatty liver disease (steatosis and steatohepatitis) that occurs in subjects even in the absence of consumption of alcohol in amounts considered harmful to the liver.

“Non-alcoholic steatohepatitis” (NASH) refers to steatohepatitis that occurs in subjects even in the absence of consumption of alcohol in amounts considered harmful to the liver. NASH can progress to cirrhosis and can be associated with hepatocellular carcinoma.

I. Biomarkers

The NAFLD and NASH biomarkers described herein were discovered using metabolomic profiling techniques. Such metabolomic profiling techniques are described in more detail in the Examples set forth below as well as in U.S. Pat. No. 7,005,255 and U.S. patent application Ser. Nos. 11/357,732, 10/695,265 (Publication No. 2005/0014132), Ser. No. 11/301,077 (Publication No. 2006/0134676), Ser. No. 11/301,078 (Publication No. 2006/0134677), Ser. No. 11/301,079 (Publication No. 2006/0134678), and Ser. No. 11/405,033, the entire contents of which are hereby incorporated herein by reference.

Generally, metabolic profiles were determined for biological samples from human subjects diagnosed with steatosis as well as from one or more other groups of human subjects (e.g., healthy control subjects not diagnosed with steatosis), as well as from human subjects diagnosed with NASH. The metabolic profile for biological samples from a subject having steatosis was compared to the metabolic profile for biological samples from the one or more other groups of subjects. Those molecules differentially present, including those molecules differentially present at a level that is statistically significant, in the metabolic profile of samples from subjects with steatosis as compared to another group (e.g., healthy control subjects not diagnosed with steatosis) were identified as biomarkers to distinguish those groups. In addition, those molecules differentially present, including those molecules differentially present at a level that is statistically significant, in the metabolic profile of samples from subjects with steatosis as compared to NASH were also identified as biomarkers to distinguish those groups.

The biomarkers are discussed in more detail herein. The biomarkers that were discovered correspond with the following group(s):

-   -   Biomarkers for distinguishing subjects having steatosis vs.         control subjects not diagnosed with liver disease (see Tables 2,         4B, 5B, 8, 11, 12, 13, FIG. 1 (gamma-glutamyltyrosine,         taurocholate, butyrylcarnitine, Metabolite-11235, Tyrosine,         uridine, glutamate, Metabolite-11304, Metabolite-4611,         Metabolite-6488, 10c-undecenoate, Metabolite-11431,         Metabolite-3107, Glutamylvaline, Metabolite-11230,         Metabolite-3083, Metabolite-11491, Metabolite-10432,         Metabolite-11422, cysteine-glutathione disulfide         (Metabolite-11728), Metabolite-10914, Metabolite-11432,         Metabolite-11314, Metabolite-12035, Metabolite-11242,         Metabolite-11529, Metabolite-11897,         gamma-glutamylphenylalanine-, Metabolite-11628, Metabolite-3108,         Alanine, glycocholate, Isobar 47 (taurochenodeoxycholic acid,         taurodeoxycholic acid), Isobar 66 (glycochenodeoxycholic acid,         glychodeoxycholic acid), Lactate, mannose, Metabolite-10026,         Metabolite-10951, Metabolite-2347, Metabolite-2821,         Metabolite-3016, Metabolite-3019, Metabolite-3025,         Metabolite-3026, Metabolite-3073, Metabolite-3077,         Metabolite-3098, Metabolite-3165, Metabolite-3330,         Metabolite-4167, Metabolite-4361, Metabolite-4759,         Metabolite-4806, Metabolite-5346, Metabolite-5769,         Metabolite-8506, Thyroxine, urate), and FIG. 3         (gamma-glutamyltyrosine, glutamate, cysteine-glutathione         disulfide (Metabolite-11728), butyrylcarnitine, taurocholate,         glutamylvaline tyrosine, Metabolite-11422, kynurenine,         glycocholate, gamma-tocopherol, Metabolite-11431, creatine,         Metabolite-10914, Metabolite-11235, hippurate, Metabolite-11529,         Metabolite-4611, Metabolite-01911, Metabolite-11491,         glycerophosphorylcholine (GPC), urate-,         gamma-glutamylphenylalanine-, Metabolite-11230, 10c-undecenoate,         Metabolite-11315, histidine, Metabolite-11304, Metabolite-11432,         Metabolite-11242, alanine, isocitrate, isoleucine, lactate,         leucine, mannose, Metabolite-10026, Metabolite-10812,         Metabolite-1496, Metabolite-1911, Metabolite-2395,         Metabolite-3016, Metabolite-3026, Metabolite-3073,         Metabolite-3098, Metabolite-3330, Metabolite-4274,         Metabolite-5769, Metabolite-7187, Metabolite-8506,         Metabolite-9855, theobromine, thryoxine, urate, valine,         xanthine));     -   Biomarkers for distinguishing subjects having NASH vs. control         subjects not diagnosed with liver disease (see Tables 1, 6B, 10,         11, 12, 13, 15, FIGS. 2 and 13 cysteine-glutathione disulfide         (Metabolite-11728), Kynurenine, glycocholate, butyrylcarnitine,         glutamate, gamma-glutamyltyrosine, hippurate, Metabolite-10914,         Metabolite-11422, gamma-tocopherol-, Metabolite-11564,         Glutamylvaline, 1-oleoylglycerophosphocholine         (Metabolite-11205), Metabolite-11431, urate, Metabolite-02272,         Tyrosine, Metabolite-11231, gamma-glutamylphenylalanine,         mannose, Metabolite-03951, Metabolite-11315, Metabolite-11529,         Metabolite-4611, 10c-undecenoate, Metabolite-11432,         Metabolite-4147, Metabolite-11227, Taurocholate,         Metabolite-11380, 3-methyl-2-oxobutyric acid, Alanine,         glutamine, isocitrate, isoleucine, leucine, meso-erythritol,         Metabolite-10026, Metabolite-10812, Metabolite-1086,         Metabolite-1110, Metabolite-1335, Metabolite-1496,         Metabolite-2041, Metabolite-2272, Metabolite-2395,         Metabolite-3073, Metabolite-3087, Metabolite-3098,         Metabolite-4274, Metabolite-5769, Metabolite-7187, Valine,         xanthine));     -   Biomarkers for distinguishing subjects having steatohepatitis         (NASH) vs. subjects having steatosis (see Tables 3, 4B, 9, 11,         12, 13, and FIG. 3 (gamma-glutamyltyrosine, glutamate,         cysteine-glutathione disulfide (Metabolite-11728),         butyrylcarnitine, taurocholate, glutamylvaline     -   tyrosine, Metabolite-11422, kynurenine, glycocholate,         gamma-tocopherol, Metabolite-11431, creatine, Metabolite-10914,         Metabolite-11235, hippurate, Metabolite-11529, Metabolite-4611,         Metabolite-01911, Metabolite-11491, glycerophosphorylcholine         (GPC), urate-, gamma-glutamylphenylalanine-, Metabolite-11230,         10c-undecenoate, Metabolite-11315, histidine, Metabolite-11304,         Metabolite-11432, Metabolite-11242, alanine, isocitrate,         isoleucine, lactate, leucine, mannose, Metabolite-10026,         Metabolite-10812, Metabolite-1496, Metabolite-1911,         Metabolite-2395, Metabolite-3016, Metabolite-3026,         Metabolite-3073, Metabolite-3098, Metabolite-3330,         Metabolite-4274, Metabolite-5769, Metabolite-7187,         Metabolite-8506, Metabolite-9855, theobromine, thryoxine, urate,         valine, xanthine));

Although the identities of some of the biomarkers compounds are not known at this time, such identities are not necessary for the identification of the biomarkers in biological samples from subjects, as the “unnamed” compounds have been sufficiently characterized by analytical techniques to allow such identification. The analytical characterization of all such “unnamed” compounds is listed in the Examples. Such “unnamed” biomarkers are designated herein using the nomenclature “Metabolite” followed by a specific metabolite number.

Any number of biomarkers may be used in the methods disclosed herein. That is, the disclosed methods may include the determination of the level(s) of one biomarker, two or more biomarkers, three or more biomarkers, four or more biomarkers, five or more biomarkers, six or more biomarkers, seven or more biomarkers, eight or more biomarkers, nine or more biomarkers, ten or more biomarkers, fifteen or more biomarkers, etc., including a combination of all of the biomarkers in each or all of Tables 1, 2, 3, 4B, 5B, 6B, 8, 9, 11, 12, 13, and 15 and FIG. 1, FIG. 3, FIG. 12, and FIG. 13 or any fraction or combination thereof. In another aspect, the number of biomarkers for use in the disclosed methods include the levels of about thirty or less biomarkers, twenty-five or less, twenty or less, fifteen or less, ten or less, nine or less, eight or less, seven or less, six or less, five or less biomarkers. In another aspect, the number of biomarkers for use in the disclosed methods include the levels of one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, twenty, twenty-five, or thirty biomarkers.

In addition, the methods disclosed herein using the biomarkers listed in the tables may be used in combination with clinical diagnostic measures of fatty liver diseases. Combinations with clinical diagnostics may facilitate the disclosed methods, or confirm results of the disclosed methods (for example, facilitating or confirming diagnosis, monitoring progression or regression, and/or determining predisposition to fatty liver diseases).

II. Diagnosis of Steatosis and/or Steatohepatitis

The identification of biomarkers for steatosis allows for the diagnosis of (or for aiding in the diagnosis of) steatosis in subjects presenting one or more symptoms of liver dysfunction. A method of diagnosing (or aiding in diagnosing) whether a subject has steatosis comprises (1) analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers of steatosis in the sample and (2) comparing the level(s) of the one or more biomarkers in the sample to steatosis-positive and/or steatosis-negative reference levels of the one or more biomarkers in order to diagnose (or aid in the diagnosis of) whether the subject has steatosis. The one or more biomarkers that are used are selected from Tables 2, 3, 4B, 5B, 8, 9, 11, 12, 13, FIG. 1, FIG. 3 and combinations thereof. When such a method is used to aid in the diagnosis of steatosis, the results of the method may be used along with other methods (or the results thereof) useful in the clinical determination of whether a subject has steatosis.

In one embodiment for the diagnosis of (or for aiding in the diagnosis of) steatosis, the method comprises (1) analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers of steatosis in the sample and (2) comparing the level(s) of the one or more biomarkers in the sample to steatosis-positive and/or steatosis-negative reference levels of the one or more biomarkers in order to diagnose (or aid in the diagnosis of) whether the subject has steatosis, and the one or more biomarkers that are used are selected from Tables 2, 3, 4B, 5B, 8, 9, 11, 12, 13, FIG. 1, FIG. 3 and combinations thereof.

In an embodiment for the diagnosis of (or for aiding in the diagnosis of) steatohepatitis, the method comprises (1) analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers of steatohepatitis in the sample and (2) comparing the level(s) of the one or more biomarkers in the sample to steatohepatitis-positive and/or steatohepatitis-negative reference levels of the one or more biomarkers in order to diagnose (or aid in the diagnosis of) whether the subject has steatohepatitis, and the one or more biomarkers that are used are selected from Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, and 15 and FIG. 2, FIG. 3, FIG. 12, FIG. 13 and combinations thereof.

In an embodiment for the diagnosis of (or for aiding in the diagnosis of) NASH, the method comprises (1) analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers of NASH in the sample and (2) comparing the level(s) of the one or more biomarkers in the sample to NASH-positive and/or NASH-negative reference levels of the one or more biomarkers in order to diagnose (or aid in the diagnosis of) whether the subject has NASH, and the one or more biomarkers that are used are selected from Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, and 15 and FIG. 2, FIG. 3, FIG. 12, FIG. 13, and combinations thereof.

Any suitable method may be used to analyze the biological sample in order to determine the level(s) of the one or more biomarkers in the sample. Suitable methods include chromatography (e.g., HPLC, gas chromatography, liquid chromatography), mass spectrometry (e.g., MS, MS-MS), enzymatic or biochemical reactions, clinical chemistry, enzyme-linked immunosorbent assay (ELISA), antibody linkage, other immunochemical techniques, and combinations thereof. Further, the level(s) of the one or more biomarkers may be measured indirectly, for example, by using an assay that measures the level of a compound (or compounds) that correlates with the level of the biomarker(s) that are desired to be measured.

The levels of one or more of the biomarkers of Tables 1, 2, 3, 4B, 5B, 6B, 8, 9, 10, 11, 12, 13, and 15 and FIG. 1, FIG. 2, FIG. 3, FIG. 12, and FIG. 15 may be determined in the methods of diagnosing and methods of aiding in diagnosing whether a subject has steatosis or steatohepatitis. For example, the level(s) of one biomarker, two or more biomarkers, three or more biomarkers, four or more biomarkers, five or more biomarkers, six or more biomarkers, seven or more biomarkers, eight or more biomarkers, nine or more biomarkers, ten or more biomarkers, etc., including a combination of all of the biomarkers in Tables 1, 2, 3, 4B, 5B, 6B, 8, 9, 10, 11, 12, 13, and 15 and FIG. 1, FIG. 2, FIG. 3, FIG. 12, and FIG. 13 or any fraction thereof, may be determined and used in such methods. Determining levels of combinations of the biomarkers may allow greater sensitivity and specificity in diagnosing steatosis and aiding in the diagnosis of steatosis, and may allow better differentiation of steatosis from other liver disorders (e.g. fibrosis, cirrhosis, liver cancer, etc.) that may have similar or overlapping biomarkers to steatosis (as compared to a subject not having steatosis). For example, ratios of the levels of certain biomarkers (and non-biomarker compounds) in biological samples may allow greater sensitivity and specificity in diagnosing steatosis and aiding in the diagnosis of steatosis and may allow better differentiation of steatosis from other liver disorders that may have similar or overlapping biomarkers to steatosis (as compared to a subject not having steatosis).

One or more biomarkers that are specific for diagnosing steatosis or steatohepatitis (or aiding in diagnosing steatosis or steatohepatitis) in a certain type of sample (e.g., liver tissue sample, liver biopsy, urine sample, or blood plasma sample) may also be used. For example, when the biological sample is plasma, one or more biomarkers listed in Tables 2, 4B, 5B, 8, 11, 12, 13, FIG. 1, FIG. 3 may be used to diagnose (or aid in diagnosing) whether a subject has steatosis.

After the level(s) of the one or more biomarkers in the sample are determined, the level(s) are compared to steatosis-positive and/or steatosis-negative reference levels (or steatohepatitis-positive and/or steatohepatitis-negative reference levels) to aid in diagnosing or to diagnose whether the subject has steatosis (or steatohepatitis). Levels of the one or more biomarkers in a sample matching the steatosis-positive reference levels (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of a diagnosis of steatosis in the subject. Levels of the one or more biomarkers in a sample matching the steatosis-negative reference levels (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of a diagnosis of no steatosis in the subject. In addition, levels of the one or more biomarkers that are differentially present (especially at a level that is statistically significant) in the sample as compared to steatosis-negative reference levels are indicative of a diagnosis of steatosis in the subject. Levels of the one or more biomarkers that are differentially present (especially at a level that is statistically significant) in the sample as compared to steatosis-positive reference levels are indicative of a diagnosis of no steatosis in the subject.

The level(s) of the one or more biomarkers may be compared to steatosis-positive and/or steatosis-negative and/or steatohepatitis-positive and/or steatohepatitis-negative reference levels using various techniques, including a simple comparison (e.g., a manual comparison) of the level(s) of the one or more biomarkers in the biological sample to steatosis-positive and/or steatosis-negative and/or steatohepatitis-positive and/or steatohepatitis-negative reference levels. The level(s) of the one or more biomarkers in the biological sample may also be compared to steatosis-positive and/or steatosis-negative and/or steatohepatitis-positive and/or steatohepatitis-negative reference levels using one or more statistical analyses (e.g., t-test, Welch's T-test, Wilcoxon's rank sum test, ANOVA, recursive partitioning, random forest).

In addition, the biological samples may be analyzed to determine the level(s) of one or more non-biomarker compounds. The level(s) of such non-biomarker compounds may also allow differentiation of steatosis from other liver disorders that may have similar or overlapping biomarkers to steatosis and/or steatohepatitis (as compared to a subject not having a liver disorder). For example, a known non-biomarker compound present in biological samples of subjects having steatosis and subjects not having steatosis could be monitored to verify a diagnosis of steatosis as compared to a diagnosis of another liver disorder when biological samples from subjects having the liver disorder do not have the non-biomarker compound.

The methods of diagnosing (or aiding in diagnosing) whether a subject has steatosis may also be conducted specifically to diagnose (or aid in diagnosing) whether a subject has steatosis and/or steatohepatitis (e.g. NASH). Such methods comprise (1) analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers of steatosis (and/or NASH) in the sample and (2) comparing the level(s) of the one or more biomarkers in the sample to steatosis-positive and/or steatosis-negative reference levels (or NASH-positive and/or NASH-negative reference levels) in order to diagnose (or aid in the diagnosis of) whether the subject has steatosis (or NASH). Biomarker specific for steatosis are listed in Tables 2, 4B, 5B, 8, 11, 12, 13, FIG. 1, FIG. 3 and biomarkers specific for NASH are listed in Tables 1, 4B, 6B, 10, 11, 12, 13, and 15 and FIG. 2, FIG. 3, and FIG. 13.

III. Methods of Distinguishing Steatosis from Steatohepatitis (NASH)

The identification of biomarkers for distinguishing steatosis versus NASH allows steatosis and NASH to be distinguished in patients. A method of distinguishing steatosis from NASH in a subject having NAFLD comprises (1) analyzing a biological sample from a subject to determine the level(s) in the sample of one or more biomarkers of steatosis that distinguish over NASH and/or one or more biomarkers of NASH that distinguish over steatosis, and (2) comparing the level(s) of the one or more biomarkers in the sample to steatosis-positive reference levels that distinguish over NASH and/or NASH-positive reference levels that distinguish over steatosis of the one or more biomarkers in order to determine whether the subject has steatosis or NASH. The one or more biomarkers that may be used include those biomarkers selected from Tables 1, 2, 3, 4B, 5B, 6B, 8, 9, 10, 11, 12, 13, and 15 and FIG. 1, FIG. 2, FIG. 3, FIG. 12, and FIG. 13 and combinations thereof.

Any suitable method may be used to analyze the biological sample in order to determine the level(s) of the one or more biomarkers in the sample. Suitable methods include chromatography (e.g., HPLC, gas chromatography, liquid chromatography), mass spectrometry (e.g., MS, MS-MS), enzymatic or biochemical reactions, clinical chemistry, enzyme-linked immunosorbent assay (ELISA), antibody linkage, other immunochemical techniques, and combinations thereof. Further, the level(s) of the one or more biomarkers may be measured indirectly, for example, by using an assay that measures the level of a compound (or compounds) that correlates with the level of the biomarker(s) that are desired to be measured.

The levels of one or more of the biomarkers of Tables 1, 2, 3, 4B, 5B, 6B, 8, 9, 10, 11, 12, 13, and 15 and FIG. 1, FIG. 2, FIG. 3, FIG. 12, and FIG. 13 may be determined in the methods of diagnosing and methods of aiding in diagnosing whether a subject has steatohepatitis. For example, the level(s) of one biomarker, two or more biomarkers, three or more biomarkers, four or more biomarkers, five or more biomarkers, six or more biomarkers, seven or more biomarkers, eight or more biomarkers, nine or more biomarkers, ten or more biomarkers, etc., including a combination of all of the biomarkers in Tables 1, 2, 3, 4B, 5B, 6B, 8, 9, 10, 11, 12, 13, and 15 and FIG. 1, FIG. 2, FIG. 3, FIG. 12, and FIG. 13 or any fraction thereof, may be determined and used in such methods. Determining levels of combinations of the biomarkers may allow greater sensitivity and specificity in distinguishing between steatosis and NASH.

One or more biomarkers that are specific for distinguishing between steatosis and NASH in a certain type of sample (e.g., liver tissue sample, urine sample, or blood plasma sample) may also be used. For example, when the biological sample is blood plasma, one or more biomarkers listed in Tables 1, 2, 3, 4B, 5B, 6B, 8, 9, 10, 11, 12, 13, FIG. 1, FIG. 2, FIG. 3 may be used.

After the level(s) of the one or more biomarkers in the sample are determined, the level(s) are compared to steatosis-positive reference levels that distinguish over NASH-negative and/or NASH-positive reference levels that distinguish over steatosis of the one or more biomarkers in order to determine whether the subject has steatosis or NASH. Levels of the one or more biomarkers in a sample matching the steatosis-positive reference levels that distinguish over NASH (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of steatosis in the subject. Levels of the one or more biomarkers in a sample matching the NASH-positive reference levels that distinguish over steatosis (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of NASH in the subject. If the level(s) of the one or more biomarkers are more similar to the steatosis-positive reference levels that distinguish over NASH (or less similar to the NASH-positive reference levels), then the results are indicative of steatosis in the subject. If the level(s) of the one or more biomarkers are more similar to the NASH-positive reference levels that distinguish over steatosis (or less similar to the steatosis-positive reference levels), then the results are indicative of NASH in the subject.

The level(s) of the one or more biomarkers may be compared to steatosis-positive reference levels that distinguish over NASH and/or NASH-positive reference levels that distinguish over steatosis using various techniques, including a simple comparison (e.g., a manual comparison) of the level(s) of the one or more biomarkers in the biological sample to steatosis-positive and/or NASH-positive reference levels. The level(s) of the one or more biomarkers in the biological sample may also be compared to steatosis-positive reference levels that distinguish over NASH and/or NASH-positive reference levels that distinguish over steatosis using one or more statistical analyses (e.g., t-test, Welch's T-test, Wilcoxon's rank sum test, ANOVA, recursive partitioning, random forest).

In addition, the biological samples may be analyzed to determine the level(s) of one or more non-biomarker compounds. The level(s) of such non-biomarker compounds may also allow differentiation of steatosis from NASH.

IV. Methods of Determining Predisposition to Steatohepatitis and/or Steatosis

The identification of biomarkers for steatosis and steatohepatitis also allows for the determination of whether a subject having no symptoms of steatohepatitis or steatosis is predisposed to developing steatohepatitis or steatosis. For example, a method of determining whether a subject having no symptoms of steatohepatitis is predisposed to developing steatohepatitis comprises (1) analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers listed in Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, and 15 and FIG. 2, FIG. 3, FIG. 12, and FIG. 13 in the sample and (2) comparing the level(s) of the one or more biomarkers in the sample to steatohepatitis-positive and/or steatohepatitis-negative reference levels of the one or more biomarkers in order to determine whether the subject is predisposed to developing steatohepatitis. The results of the method may be used along with other methods (or the results thereof) useful in the clinical determination of whether a subject is predisposed to developing steatohepatitis.

As described above in connection with methods of diagnosing (or aiding in the diagnosis of) steatohepatitis, any suitable method may be used to analyze the biological sample in order to determine the level(s) of the one or more biomarkers in the sample.

As with the methods of diagnosing (or aiding in the diagnosis of) steatohepatitis or steatosis described above, the level(s) of one biomarker, two or more biomarkers, three or more biomarkers, four or more biomarkers, five or more biomarkers, six or more biomarkers, seven or more biomarkers, eight or more biomarkers, nine or more biomarkers, ten or more biomarkers, etc., including a combination of all of the biomarkers, for example, in Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, and 15 and FIG. 2, FIG. 3, FIG. 12, and FIG. 13 or any fraction thereof, may be determined and used in methods of determining whether a subject having no symptoms of steatohepatitis is predisposed to developing steatohepatitis.

After the level(s) of the one or more biomarkers in the sample are determined, the level(s) are compared to, for example, steatohepatitis-positive and/or steatohepatitis-negative reference levels in order to predict whether the subject is predisposed to developing steatohepatitis. Levels of the one or more biomarkers in a sample matching the steatohepatitis-positive reference levels (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of the subject being predisposed to developing steatohepatitis. Levels of the one or more biomarkers in a sample matching the steatohepatitis-negative reference levels (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of the subject not being predisposed to developing steatohepatitis. In addition, levels of the one or more biomarkers that are differentially present (especially at a level that is statistically significant) in the sample as compared to steatohepatitis-negative reference levels are indicative of the subject being predisposed to developing steatohepatitis. Levels of the one or more biomarkers that are differentially present (especially at a level that is statistically significant) in the sample as compared to steatohepatitis-positive reference levels are indicative of the subject not being predisposed to developing steatohepatitis.

Furthermore, it may also be possible to determine reference levels specific to assessing whether or not a subject that does not have steatohepatitis is predisposed to developing steatohepatitis. For example, it may be possible to determine reference levels of the biomarkers for assessing different degrees of risk (e.g., low, medium, high) in a subject for developing steatohepatitis. Such reference levels could be used for comparison to the levels of the one or more biomarkers in a biological sample from a subject.

As with the methods described above, the level(s) of the one or more biomarkers may be compared to steatohepatitis-positive and/or steatohepatitis-negative reference levels using various techniques, including a simple comparison, one or more statistical analyses, and combinations thereof.

As with the methods of diagnosing (or aiding in diagnosing) whether a subject has steatohepatitis and/or steatosis, the methods of determining whether a subject having no symptoms of steatohepatitis or steatosis is predisposed to developing steatosis or steatohepatitis may further comprise analyzing the biological sample to determine the level(s) of one or more non-biomarker compounds.

The methods of determining whether a subject having no symptoms of steatohepatitis is predisposed to developing steatohepatitis may also be conducted specifically to determine whether a subject having no symptoms of steatohepatitis is predisposed to developing NASH. Biomarkers specific for steatosis are listed in Tables 2, 4B, 5B, 8, 11, 12, 13, FIG. 1, FIG. 3 and biomarkers specific for NASH are listed in Tables 1, 4B, 6B, 10, 11, 12, 13, and 15 and FIG. 2, FIG. 3, FIG. 12, and FIG. 13.

In addition, methods of determining whether a subject having steatosis is predisposed to developing NASH may be conducted using one or more biomarkers selected from Tables 1, 4B, 6B, 10, 11, 12, 13, and 15 and FIG. 2, FIG. 3, FIG. 12, and FIG. 13.

V. Methods of Monitoring Progression/Regression of Steatosis and/or Steatohepatitis

The identification of biomarkers for steatosis and steatohepatitis also allows for monitoring progression/regression of steatosis and/or steatohepatitis in a subject. For example, a method of monitoring the progression/regression of steatosis in a subject comprises (1) analyzing a first biological sample from a subject to determine the level(s) of one or more biomarkers for steatosis selected from Tables 2, 3, 4B, 6B, 8, 9, 11, 12, 13, FIG. 1, FIG. 3, the first sample obtained from the subject at a first time point, (2) analyzing a second biological sample from a subject to determine the level(s) of the one or more biomarkers, the second sample obtained from the subject at a second time point, and (3) comparing the level(s) of one or more biomarkers in the first sample to the level(s) of the one or more biomarkers in the second sample in order to monitor the progression/regression of steatosis in the subject. The results of the method are indicative of the course of steatosis (i.e., progression or regression, if any change) in the subject.

The change (if any) in the level(s) of the one or more biomarkers over time may be indicative of progression or regression of steatosis in the subject. In order to characterize the course of steatosis in the subject, the level(s) of the one or more biomarkers in the first sample, the level(s) of the one or more biomarkers in the second sample, and/or the results of the comparison of the levels of the biomarkers in the first and second samples may be compared to steatosis-positive, steatosis-negative, NAFLD-positive, NAFLD-negative, high-grade steatosis-positive, and/or NASH-negative reference levels as well as steatosis-positive reference levels that distinguish over NASH and/or NASH-positive reference levels that distinguish over steatosis. If the comparisons indicate that the level(s) of the one or more biomarkers are increasing or decreasing over time (e.g., in the second sample as compared to the first sample) to become more similar to the steatosis-positive reference levels (or less similar to the steatosis-negative reference levels), to the NASH reference levels, or, when the subject initially has steatosis, to the NASH-positive reference levels that distinguish over steatosis, then the results are indicative of steatosis progression. If the comparisons indicate that the level(s) of the one or more biomarkers are increasing or decreasing over time to become more similar to the steatosis-negative reference levels (or less similar to the steatosis-positive reference levels), or, when the subject initially has NASH, to steatosis reference levels and/or to steatosis-positive reference levels that distinguish over NASH, then the results are indicative of steatosis regression.

As with the other methods described herein, the comparisons made in the methods of monitoring progression/regression of steatosis in a subject may be carried out using various techniques, including simple comparisons, one or more statistical analyses, and combinations thereof.

The results of the method may be used along with other methods (or the results thereof) useful in the clinical monitoring of progression/regression of steatosis in a subject.

As described above in connection with methods of diagnosing (or aiding in the diagnosis of) steatosis, any suitable method may be used to analyze the biological samples in order to determine the level(s) of the one or more biomarkers in the samples. In addition, the level(s) of one or more biomarkers, including a combination of all of the biomarkers in Tables 2, 4B, 5B, 8, 11, 12, 13, and 15 and FIG. 1, FIG. 3, FIG. 12, and FIG. 13 or any fraction thereof, may be determined and used in methods of monitoring progression/regression of steatosis in a subject.

Such methods could be conducted to monitor the course of steatosis in subjects having steatosis or could be used in subjects not having steatosis (e.g., subjects suspected of being predisposed to developing steatosis) in order to monitor levels of predisposition to steatosis.

Such methods could be conducted to monitor the course of steatohepatitis in subjects having steatohepatitis or could be used in subjects not having steatohepatitis (e.g., subjects suspected of being predisposed to developing steatohepatitis) in order to monitor levels of predisposition to steatohepatitis. The levels(s) of one or more biomarkers, including a combination of all of the biomarkers in Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, and 15 and FIGS. 2, 3, 12, and FIG. 13, or any fraction thereof, may be determined and used in methods of monitoring progression/regression of steatohepatitis in a subject.

VI. Methods of Assessing Efficacy of Compositions for Treating Steatohepatitis and/or Steatosis

The identification of biomarkers for steatohepatitis and steatosis also allows for assessment of the efficacy of a composition for treating steatohepatitis and/or steatosis as well as the assessment of the relative efficacy of two or more compositions for treating steatohepatitis and/or steatosis. Such assessments may be used, for example, in efficacy studies as well as in lead selection of compositions for treating steatohepatitis or steatosis.

For example, a method of assessing the efficacy of a composition for treating steatohepatitis comprises (1) analyzing, from a subject having steatohepatitis and currently or previously being treated with a composition, a biological sample to determine the level(s) of one or more biomarkers selected from Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, and 15 and FIG. 2, FIG. 3, FIG. 12, and FIG. 13, and (2) comparing the level(s) of the one or more biomarkers in the sample to (a) level(s) of the one or more biomarkers in a previously-taken biological sample from the subject, wherein the previously-taken biological sample was obtained from the subject before being treated with the composition, (b) steatohepatitis-positive reference levels (including NASH-positive reference levels) of the one or more biomarkers, (c) steatohepatitis-negative reference levels (including NASH-negative reference levels) of the one or more biomarkers, (d) NASH-positive reference levels that distinguish over steatosis, and/or (e) steatosis-positive reference levels that distinguish over NASH. The results of the comparison are indicative of the efficacy of the composition for treating steatohepatitis.

Thus, in order to characterize the efficacy of the composition for treating steatohepatitis, the level(s) of the one or more biomarkers in the biological sample are compared to (1) steatohepatitis-positive reference levels, (2) steatohepatitis-negative reference levels, (3) previous levels of the one or more biomarkers in the subject before treatment with the composition, (4) NASH-positive reference levels that distinguish over steatosis, and/or (5) steatosis-positive reference levels that distinguish over NASH.

When comparing the level(s) of the one or more biomarkers in the biological sample (from a subject having steatohepatitis and currently or previously being treated with a composition) to steatohepatitis-positive reference levels and/or steatohepatitis-negative reference levels, level(s) in the sample matching the steatohepatitis-negative reference levels (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of the composition having efficacy for treating steatohepatitis. Levels of the one or more biomarkers in the sample matching the steatohepatitis-positive reference levels (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of the composition not having efficacy for treating steatohepatitis. The comparisons may also indicate degrees of efficacy for treating steatohepatitis based on the level(s) of the one or more biomarkers.

When comparing the level(s) of the one or more biomarkers in the biological sample (from a subject having NASH and currently or previously being treated with a composition) NASH-positive reference levels that distinguish over steatosis and/or steatosis-positive reference levels that distinguish over NASH, level(s) in the sample matching the NASH-positive reference levels that distinguish over steatosis (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of the composition having efficacy for treating steatohepatitis. Levels of the one or more biomarkers in the sample matching the NASH-positive reference levels that distinguish over steatosis (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of the composition not having efficacy for treating steatohepatitis.

When the level(s) of the one or more biomarkers in the biological sample (from a subject having steatohepatitis and currently or previously being treated with a composition) are compared to level(s) of the one or more biomarkers in a previously-taken biological sample from the subject before treatment with the composition, any changes in the level(s) of the one or more biomarkers are indicative of the efficacy of the composition for treating steatohepatitis. That is, if the comparisons indicate that the level(s) of the one or more biomarkers have increased or decreased after treatment with the composition to become more similar to the steatohepatitis-negative reference levels (or less similar to the steatohepatitis-positive reference levels) or, when the subject initially has steatosis, the level(s) have increased or decreased to become more similar to NASH-positive reference levels that distinguish over steatosis (or less similar to the steatosis-positive reference levels that distinguish over NASH), then the results are indicative of the composition having efficacy for treating steatohepatitis. If the comparisons indicate that the level(s) of the one or more biomarkers have not increased or decreased after treatment with the composition to become more similar to the steatohepatitis-negative reference levels (or less similar to the steatohepatitis-positive reference levels) or, when the subject initially has NASH, the level(s) have not increased or decreased to become more similar to steatosis-positive reference levels that distinguish over NASH (or less similar to the NASH-positive reference levels that distinguish over steatosis), then the results are indicative of the composition not having efficacy for treating steatohepatitis. The comparisons may also indicate degrees of efficacy for treating steatohepatitis based on the amount of changes observed in the level(s) of the one or more biomarkers after treatment. In order to help characterize such a comparison, the changes in the level(s) of the one or more biomarkers, the level(s) of the one or more biomarkers before treatment, and/or the level(s) of the one or more biomarkers in the subject currently or previously being treated with the composition may be compared to steatohepatitis-positive reference levels (including low grade and NASH-positive reference levels), steatohepatitis-negative reference levels (including low grade and NASH-negative reference levels), steatosis-positive reference levels that distinguish over NASH, and/or NASH-positive reference levels that distinguish over steatosis.

Another method for assessing the efficacy of a composition in treating steatohepatitis comprises (1) analyzing a first biological sample from a subject to determine the level(s) of one or more biomarkers selected from Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, and 15 and FIG. 2, FIG. 3, FIG. 12, and FIG. 13, the first sample obtained from the subject at a first time point, (2) administering the composition to the subject, (3) analyzing a second biological sample from a subject to determine the level(s) of the one or more biomarkers, the second sample obtained from the subject at a second time point after administration of the composition, and (4) comparing the level(s) of one or more biomarkers in the first sample to the level(s) of the one or more biomarkers in the second sample in order to assess the efficacy of the composition for treating steatohepatitis. As indicated above, if the comparison of the samples indicates that the level(s) of the one or more biomarkers have increased or decreased after administration of the composition to become more similar to the steatohepatitis-negative reference levels (or less similar to the steatohepatitis-positive reference levels) or, when the subject initially has NASH, if the level(s) have increased or decreased to become more similar to steatosis-positive reference levels that distinguish over NASH (or less similar to the NASH-positive reference levels that distinguish over steatosis), then the results are indicative of the composition having efficacy for treating steatohepatitis. If the comparisons indicate that the level(s) of the one or more biomarkers have not increased or decreased after treatment with the composition to become more similar to the steatohepatitis-negative reference levels (or less similar to the steatohepatitis-positive reference levels) or, when the subject initially has NASH, the level(s) have not increased or decreased to become more similar to steatosis-positive reference levels that distinguish over NASH (or less similar to the NASH-positive reference levels that distinguish over steatosis), then the results are indicative of the composition not having efficacy for treating steatohepatitis.

The comparison may also indicate a degree of efficacy for treating steatohepatitis based on the amount of changes observed in the level(s) of the one or more biomarkers after administration of the composition as discussed above.

A method of assessing the relative efficacy of two or more compositions for treating steatohepatitis comprises (1) analyzing, from a first subject having steatohepatitis and currently or previously being treated with a first composition, a first biological sample to determine the level(s) of one or more biomarkers selected from Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, and 15 and FIG. 2, FIG. 3, FIG. 12, and FIG. 13 (2) analyzing, from a second subject having steatohepatitis and currently or previously being treated with a second composition, a second biological sample to determine the level(s) of the one or more biomarkers, and (3) comparing the level(s) of one or more biomarkers in the first sample to the level(s) of the one or more biomarkers in the second sample in order to assess the relative efficacy of the first and second compositions for treating steatohepatitis. The results are indicative of the relative efficacy of the two compositions, and the results (or the levels of the one or more biomarkers in the first sample and/or the level(s) of the one or more biomarkers in the second sample) may be compared to steatohepatitis-positive reference levels (including low grade and NASH-positive reference levels), steatohepatitis-negative reference levels (including low grade and NASH-negative reference levels), steatosis-positive reference levels that distinguish over NASH, and/or NASH-positive reference levels that distinguish over steatosis to aid in characterizing the relative efficacy.

Each of the methods of assessing efficacy may be conducted on one or more subjects or one or more groups of subjects (e.g., a first group being treated with a first composition and a second group being treated with a second composition).

As with the other methods described herein, the comparisons made in the methods of assessing efficacy (or relative efficacy) of compositions for treating steatohepatitis and/or steatosis may be carried out using various techniques, including simple comparisons, one or more statistical analyses, and combinations thereof. Any suitable method may be used to analyze the biological samples in order to determine the level(s) of the one or more biomarkers in the samples. In addition, the level(s) of one or more biomarkers, including a combination of all of the biomarkers in Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, and 15 and FIG. 2, FIG. 3, FIG. 12, and FIG. 13 or any fraction thereof, may be determined and used in methods of assessing efficacy (or relative efficacy) of compositions for treating steatohepatitis.

Finally, the methods of assessing efficacy (or relative efficacy) of one or more compositions for treating steatohepatitis may further comprise analyzing the biological sample to determine the level(s) of one or more non-biomarker compounds. The non-biomarker compounds may then be compared to reference levels of non-biomarker compounds for subjects having (or not having) steatohepatitis.

VII. Methods of Screening a Composition for Activity in Modulating Biomarkers Associated with Steatohepatitis and/or Steatosis

The identification of biomarkers for steatohepatitis and steatosis also allows for the screening of compositions for activity in modulating biomarkers associated with steatohepatitis and/or steatosis, which may be useful in treating steatohepatitis and/or steatosis. For example, methods of screening compositions useful for treatment of steatohepatitis comprise assaying test compositions for activity in modulating the levels of one or more biomarkers in Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, and 15 and FIG. 2, FIG. 3, FIG. 12, and FIG. 13. Such screening assays may be conducted in vitro and/or in vivo, and may be in any form known in the art useful for assaying modulation of such biomarkers in the presence of a test composition such as, for example, cell culture assays, organ culture assays, and in vivo assays (e.g., assays involving animal models).

In one embodiment, a method for screening a composition for activity in modulating one or more biomarkers of steatohepatitis comprises (1) contacting one or more cells with a composition, (2) analyzing at least a portion of the one or more cells or a biological sample associated with the cells to determine the level(s) of one or more biomarkers of steatohepatitis selected from Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, and 15 and FIG. 2, FIG. 3, FIG. 12, and FIG. 13; and (3) comparing the level(s) of the one or more biomarkers with predetermined standard levels for the one or more biomarkers to determine whether the composition modulated the level(s) of the one or more biomarkers. As discussed above, the cells may be contacted with the composition in vitro and/or in vivo. The predetermined standard levels for the one or more biomarkers may be the levels of the one or more biomarkers in the one or more cells in the absence of the composition. The predetermined standard levels for the one or more biomarkers may also be the level(s) of the one or more biomarkers in control cells not contacted with the composition.

In addition, the methods may further comprise analyzing at least a portion of the one or more cells or a biological sample associated with the cells to determine the level(s) of one or more non-biomarker compounds of steatohepatitis and/or steatosis. The levels of the non-biomarker compounds may then be compared to predetermined standard levels of the one or more non-biomarker compounds.

Any suitable method may be used to analyze at least a portion of the one or more cells or a biological sample associated with the cells in order to determine the level(s) of the one or more biomarkers (or levels of non-biomarker compounds). Suitable methods include chromatography (e.g., HPLC, gas chromatograph, liquid chromatography), mass spectrometry (e.g., MS, MS-MS), enzymatic or biochemical reactions, clinical chemistry, ELISA, antibody linkage, other immunochemical techniques, and combinations thereof. Further, the level(s) of the one or more biomarkers (or levels of non-biomarker compounds) may be measured indirectly, for example, by using an assay that measures the level of a compound (or compounds) that correlates with the level of the biomarker(s) (or non-biomarker compounds) that are desired to be measured.

VIII. Method of Identifying Potential Drug Targets

The identification of biomarkers for steatohepatitis and steatosis also allows for the identification of potential drug targets for steatohepatitis and/or steatosis. For example, a method for identifying a potential drug target for steatohepatitis comprises (1) identifying one or more biochemical pathways associated with one or more biomarkers for steatohepatitis selected from Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, and 15 and FIG. 2, FIG. 3, FIG. 12, and FIG. 13, and (2) identifying a protein (e.g., an enzyme) affecting at least one of the one or more identified biochemical pathways, the protein being a potential drug target for steatohepatitis.

Another method for identifying a potential drug target for steatohepatitis comprises (1) identifying one or more biochemical pathways associated with one or more biomarkers for steatohepatitis selected from Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, and 15 and FIG. 2, FIG. 3, FIG. 12, and FIG. 13, and one or more non-biomarker compounds of steatohepatitis and (2) identifying a protein affecting at least one of the one or more identified biochemical pathways, the protein being a potential drug target for steatohepatitis.

One or more biochemical pathways (e.g., biosynthetic and/or metabolic (catabolic) pathway) are identified that are associated with one or more biomarkers (or non-biomarker compounds). After the biochemical pathways are identified, one or more proteins affecting at least one of the pathways are identified. Preferably, those proteins affecting more than one of the pathways are identified.

A build-up of one metabolite (e.g., a pathway intermediate) may indicate the presence of a ‘block’ downstream of the metabolite and the block may result in a low/absent level of a downstream metabolite (e.g. product of a biosynthetic pathway). In a similar manner, the absence of a metabolite could indicate the presence of a ‘block’ in the pathway upstream of the metabolite resulting from inactive or non-functional enzyme(s) or from unavailability of biochemical intermediates that are required substrates to produce the product. Alternatively, an increase in the level of a metabolite could indicate a genetic mutation that produces an aberrant protein which results in the over-production and/or accumulation of a metabolite which then leads to an alteration of other related biochemical pathways and result in dysregulation of the normal flux through the pathway; further, the build-up of the biochemical intermediate metabolite may be toxic or may compromise the production of a necessary intermediate for a related pathway. It is possible that the relationship between pathways is currently unknown and this data could reveal such a relationship.

The proteins identified as potential drug targets may then be used to identify compositions that may be potential candidates for treating steatohepatitis, including compositions for gene therapy.

IX. Methods of Treating Steatohepatitis and/or Steatosis

The identification of biomarkers for steatohepatitis and steatosis also allows for the treatment of steatohepatitis and/or steatosis. For example, in order to treat a subject having steatohepatitis, an effective amount of one or more steatohepatitis biomarkers that are lowered in steatohepatitis as compared to a healthy subject not having steatohepatitis may be administered to the subject. The biomarkers that may be administered may comprise one or more of the biomarkers in Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, 15 and FIG. 2, FIG. 3, FIG. 12, and FIG. 13 that are decreased in steatohepatitis. In some embodiments, the biomarkers that are administered are one or more biomarkers listed in Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, and 15 and FIG. 2, FIG. 3, FIG. 12, and FIG. 13 that are decreased in steatohepatitis and that have a p-value less than 0.10. In other embodiments, the biomarkers that are administered are one or biomarkers listed in Tables 1, 3, 4B, 6B, 9, 10, 11, 12, 13, and 15 and FIG. 2, FIG. 3, FIG. 12, and FIG. 13 that are decreased in steatohepatitis by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%, by at least 80%, by at least 85%, by at least 90%, by at least 95%, or by 100% (i.e., absent).

X. Methods of Using the Steatohepatitis and Steatosis Biomarkers for Other Liver Disorders

It is believed that some of the biomarkers for steatosis and steatohepatitis described herein may also be biomarkers for liver disorders (e.g. liver fibrosis, cirrhosis, liver cancer, etc.) in general. Therefore, it is believed that at least some of the steatosis biomarkers or steatohepatitis biomarkers may be used in the methods described herein for liver disorders in general. That is, the methods described herein with respect to steatosis and/or steatohepatitis may also be used for diagnosing (or aiding in the diagnosis of) a liver disorder, methods of monitoring progression/regression of a liver disorder, methods of assessing efficacy of compositions for treating a liver disorder, methods of screening a composition for activity in modulating biomarkers associated with a liver disorder, methods of identifying potential drug targets for liver disorder, and methods of treating a liver disorder. Such methods could be conducted as described herein with respect to steatosis.

XI. Other Methods

Other methods of using the biomarkers discussed herein are also contemplated. For example, the methods described in U.S. Pat. No. 7,005,255 and U.S. patent application Ser. No. 10/695,265 may be conducted using a small molecule profile comprising one or more of the biomarkers disclosed herein.

In any of the methods listed herein, the biomarkers that are used may be selected from those biomarkers in Tables 1, 2, 3, 4B, 5B, 6B, 8, 9, 10, 11, 12, 13, and 15 and FIGS. 1, 2, 3, 12, and 13, having p-values of less than 0.05 and/or those biomarkers in Tables 1, 2, 3, 4B, 5B, 6B, 8, 9, 10, 11, 12, 13, and 15 and FIGS. 1, 2, 3, 12, and 13 having q-values of less than 0.10. The biomarkers that are used in any of the methods described herein may also be selected from those biomarkers in Tables 1, 2, 3, 4B, 5B, 6B, 8, 9, 10, 11, 12, 13, and 15 and FIGS. 1, 2, 3, 12, and 13 that are decreased in steatosis and/or steatohepatitis (as compared to the control) or that are decreased in remission (as compared to control or steatosis and/or steatohepatitis) by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%, by at least 80%, by at least 85%, by at least 90%, by at least 95%, or by 100% (i.e., absent); and/or those biomarkers in Tables 1, 2, 3, 4B, 5B, 6B, 8, 9, 10, 11, 12, 13, and 15 and FIG. 1, FIG. 2, FIG. 3, FIG. 12, and FIG. 13 that are increased in steatosis and/or steatohepatitis (as compared to the control or remission) or that are increased in remission (as compared to the control or steatosis or steatohepatitis) by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%, by at least 80%, by at least 85%, by at least 90%, by at least 95%, by at least 100%, by at least 110%, by at least 120%, by at least 130%, by at least 140%, by at least 150%, or more.

EXAMPLES

The invention will be further explained by the following illustrative examples that are intended to be non-limiting.

I. General Methods

A. Identification of Metabolic Profiles for Steatosis and Steatohepatitis

Each sample was analyzed to determine the concentration of several hundred metabolites. Analytical techniques such as GC-MS (gas chromatography-mass spectrometry) and LC-MS (liquid chromatography-mass spectrometry) were used to analyze the metabolites. Multiple aliquots were simultaneously, and in parallel, analyzed, and, after appropriate quality control (QC), the information derived from each analysis was recombined. Every sample was characterized according to several thousand characteristics, which ultimately amount to several hundred chemical species. The techniques used were able to identify novel and chemically unnamed compounds.

B. Statistical Analysis

The data was analyzed using statistical tests of significance to identify molecules (either known, named metabolites or unnamed metabolites) present at differential levels in a definable population or subpopulation (e.g., biomarkers for steatosis biological samples compared to control biological samples or compared to patients in remission from steatosis) useful for distinguishing between the definable populations (e.g., steatosis and control, steatohepatitis (NASH) and control, steatosis and NASH). Other molecules (either known, named metabolites or unnamed metabolites) in the definable population or subpopulation were also identified. For significance tests, Analysis of variance (ANOVA), analysis of covariance (ANCOVA) and Wilcoxon signed rank test were used to statistically analyze the data. For classification tests, Random Forest and Recursive Partitioning were used to analyze the data.

C. Biomarker identification

Various peaks identified in the analyses (e.g. GC-MS, LC-MS, MS-MS), including those identified as statistically significant, were subjected to a mass spectrometry based chemical identification process.

Example 1

Biomarkers were discovered by (1) analyzing blood plasma samples from different groups of human subjects to determine the levels of metabolites in the samples and then (2) statistically analyzing the results to determine those metabolites that were differentially present in the two groups.

The plasma samples used for the analysis were 25 control samples that were from healthy subjects, 11 samples from patients with steatosis and 24 samples from patients with NASH. Subjects for all groups included males and females. After the levels of metabolites were determined, the data was analyzed using significance tests (ANOVA, ANCOVA, Wilcoxon).

ANOVA was used to identify significant differences in the mean levels of metabolites between two populations (i.e., Steatosis vs. Control, NASH vs. Control, Steatosis vs. NASH).

Biomarkers:

As listed below in Tables 1, 2 and 3, biomarkers were discovered that were differentially present between plasma samples from steatosis patients and Control subjects, biomarkers that were discovered that were differentially present between plasma samples from patients with NASH and from Control subjects and biomarkers that were discovered that were differentially present between plasma samples from steatohepatitis (NASH) and plasma samples from subjects with steatosis.

Tables 1, 2 and 3 include, for each listed biomarker, the p-value and q-value determined in the statistical analysis of the data concerning the biomarkers in the steatosis mean level as compared to the control mean level, the NASH mean level as compared to the control mean level, and the steatohepatitis (NASH) mean level as compared to the steatosis mean level. Library indicates the chemical library that was used to identify the compounds. The number 50 refer to the GC library and the numbers 61, 200 and 201 refer to the LC library. Comp ID refers to the identification number for the compound in our internal chemical compound database. The data is presented as a percent change based upon the ratio of means and indicates biomarkers that increase or decrease in NASH relative to control (Table 1), in steatosis relative to control (Table 2) and/or in NASH relative to steatosis (Table 3).

TABLE 1 Biomarkers from subjects with steatohepatitis (NASH) compared to Control subjects ID Biomarker Library p-value q-value % Change 12751 Metabolite-3073 50 1.36E−07 1.08E−05 129% 32322 glutamate 50 1.52E−07 3.11E−05 137% 1113 isocitrate 61 3.94E−07 0.000021 85% 1125 isoleucine 50  6.7E−07 2.27E−05 52% 18706 Metabolite-5769 61 7.12E−07 2.27E−05 64% 33073 cysteine-glutathione disulfide 200 1.36E−06 0.0001 −57% (Metabolite-11728) 60 leucine 50 3.12E−06 0.0001 11% 16511 Metabolite-4274 50 3.22E−06 0.0001 61% 32868 glycocholate* 201 8.56E−06 0.0005 331% 32393 glutamylvaline 200 8.94E−06 0.0005 33% 1126 alanine 50 0.000011 0.0002 40% 1299 tyrosine 61 1.77E−05 0.0003 30% 24285 Metabolite-10026 61 2.22E−05 0.0003 52% 2734 gamma-glutamyltyrosine 200 2.23E−05 0.0009 29% 15140 kynurenine 61 2.96E−05 0.0004 33% 31489 Metabolite-10914 50 4.45E−05 0.0014 −45% 1647 glutamine 50 0.0001 0.0007 37% 8509 Metabolite-2041 61 0.0001 0.0008 34% 12780 Metabolite-3098 50 0.0001 0.0007 40% 17068 Metabolite-4627 61 0.0001 0.0009 250% 20267 Metabolite-7187 61 0.0001 0.001 85% 32701 urate- 200 0.0001 0.0023 22% 33362 gamma-glutamylphenylalanine- 200 0.0001 0.0023 29% 33420 gamma-tocopherol- 50 0.0001 0.0023 87% 12774 Metabolite-3094 50 0.0001 0.0023 52% 16865 Metabolite-4522 50 0.0001 0.0011 30% 20699 erythritol 50 0.0001 0.0007 36% 30821 Metabolite-10812 50 0.0001 0.0007 21% 2761 thyroxine 61 0.0002 0.0013 46% 6847 Metabolite-1496 61 0.0002 0.0014 50% 13600 Metabolite-3330 61 0.0002 0.0014 362% 18882 Isobar-47-includes- 61 0.0002 0.0014 268% taurochenodeoxycholic acid- and-taurodeoxycholic acid 32497 10c-undecenoate 201 0.0002 0.003 −9% 32675 Metabolite-03951 200 0.0002 0.003 25% 32881 Metabolite-11564 201 0.0002 0.003 33% 584 mannose 50 0.0002 0.0012 29% 1336 palmitate (16:0) 50 0.0002 0.0012 10% 16650 Metabolite-4360 50 0.0002 0.0012 213% 5628 Metabolite-1086 61 0.0003 0.0016 105% 24233 Metabolite-9855 61 0.0003 0.0017 146% 18497 taurocholate 201 0.0003 0.0042 303% 22570 Metabolite-9033 50 0.0003 0.0017 1% 5687 Metabolite-1110 61 0.0004 0.0021 −71% 59 histidine 201 0.0004 0.0042 −14% 32863 Metabolite-11546 201 0.0004 0.0042 223% 33133 Metabolite-11788 200 0.0004 0.0042 20% 64 phenylalanine 61 0.0004 0.002 13% 31595 Metabolite-10951 61 0.0005 0.0022 60% 12783 Metabolite-3101 50 0.0005 0.0042 61% 15990 glycerophosphorylcholine 200 0.0005 0.0042 −28% (GPC) 17028 Metabolite-4611 50 0.0005 0.0046 35% 32632 Metabolite-11315 200 0.0005 0.0042 −32% 12767 Metabolite-3087 50 0.0006 0.0025 46% 12791 Metabolite-3109 50 0.0006 0.0025 −28% 32564 Metabolite-11247 201 0.0006 0.0048 −67% 32808 Metabolite-11491 201 0.0006 0.0048 130% 527 lactate 50 0.0006 0.0025 26% 21047 3-methyl-2-oxobutyrate 61 0.0006 0.0025 10% 7933 Metabolite-1911 61 0.0007 0.0027 112% 31510 Metabolite-10932 61 0.0007 0.0028 34% 32735 Metabolite-01911 200 0.0007 0.0052 99% 1358 stearate (18:0) 50 0.0008 0.0029 −1% 3147 xanthine 61 0.0009 0.0031 91% 27801 Metabolite-10589 61 0.0009 0.0031 64% 1494 5-oxoproline 50 0.0009 0.0031 0% 32552 Metabolite-11235 201 0.001 0.0065 90% 32739 Metabolite-11422 201 0.001 0.0066 31% 599 pyruvate 50 0.001 0.0065 44% 25602 Metabolite-10432 50 0.001 0.0065 73% 10551 Metabolite-2347 61 0.0011 0.0036 214% 16016 Metabolite-3994 61 0.0011 0.0037 −45% 32846 Metabolite-11529 201 0.0011 0.0071 151% 13296 Metabolite-3230 61 0.0013 0.004 25% 32519 1- 200 0.0013 0.0077 −28% linoleoylglycerophosphocholine (Metabolite-11205) 32549 Metabolite-02269 201 0.0014 0.0077 −67% 32550 Metabolite-02272 201 0.0014 0.0077 −28% 8210 Metabolite-1981 61 0.0015 0.0048 −41% 15753 hippurate 201 0.0016 0.0085 −32% 32656 Metabolite-11339 201 0.0017 0.0087 −10% 13214 Metabolite-3183 61 0.0018 0.0055 30% 32544 Metabolite-11227 201 0.0019 0.0096 13% 10286 Metabolite-2272 61 0.002 0.006 −26% 22803 Isobar-66-includes- 61 0.002 0.006 195% glycochenodeoxycholic acid- glycodeoxycholic acid 10245 Metabolite-2269- 61 0.0021 0.006 −62% 32548 Metabolite-11231 201 0.0022 0.0108 82% 32559 Metabolite-11242 201 0.0022 0.0108 60% 1110 arachidonate-20-4-n-6- 50 0.0023 0.0064 11% 32637 Metabolite-11320 201 0.0024 0.0115 −27% 10715 Metabolite-2395 61 0.0025 0.0068 107% 32813 Metabolite-11496 201 0.0027 0.0122 −18% 32412 butyrylcarnitine 200 0.0029 0.0129 27% 10672 Metabolite-2390 61 0.0031 0.0084 35% 15529 Metabolite-3951 61 0.0034 0.009 27% 16308 Metabolite-4147 50 0.0034 0.015 43% 32641 Metabolite-11324 201 0.0037 0.0159 −25% 32786 Metabolite-11469 200 0.0039 0.0163 −68% 32346 glycochenodeoxycholate 201 0.004 0.0165 102% 20488 glucose 50 0.004 0.0105 18% 607 urocanate 200 0.0041 0.0165 −20% 7941 Metabolite-1914 61 0.0043 0.0112 −69% 32748 Metabolite-11431 201 0.0045 0.0179 73% 21421 Metabolite-8214 50 0.0051 0.013 30% 15122 glycerol 50 0.0053 0.0131 18% 15996 aspartate 50 0.0055 0.021 40% 18657 Metabolite-5726 61 0.0057 0.0137 65% 32910 Metabolite-11593 201 0.0057 0.021 23% 33131 Metabolite-11786 200 0.0057 0.021 −34% 17627 Metabolite-4986 50 0.0057 0.0137 −26% 22600 Metabolite-9043 50 0.0057 0.0137 28% 30728 Metabolite-10797 61 0.0064 0.015 72% 32547 Metabolite-11230 201 0.0065 0.0232 70% 32854 Metabolite-11537 200 0.0066 0.0232 −47% 32752 Metabolite-11435 201 0.0067 0.0233 68% 12644 Metabolite-3016 50 0.0072 0.0164 14% 18118 Metabolite-5346 50 0.0072 0.0164 31% 27710 N-acetylglycine 50 0.0076 0.017 −32% 32749 Metabolite-11432 201 0.0079 0.027 71% 18291 3-4-5-trimethoxycinnamic acid 61 0.008 0.0178 −64% 19462 Metabolite-6446 50 0.0083 0.0181 24% 3141 Betaine 200 0.0084 0.0283 −16% 10414 Metabolite-2291 61 0.0086 0.0186 −79% 33386 Metabolite-12035 50 0.0086 0.0284 42% 16496 Metabolite-4251 50 0.0087 0.0186 36% 1769 Cortisone 201 0.0089 0.0287 −22% 15500 carnitine 200 0.009 0.0287 10% 18369 gamma-glutamylleucine 200 0.0093 0.0294 19% 32848 Metabolite-11531 201 0.0095 0.0296 78% 16518 Metabolite-4276 50 0.0098 0.0205 79% 32545 Metabolite-11228 201 0.0099 0.0303 79% 19323 docosahexaenoate-DHA- 50 0.0101 0.0209 −27% 19490 Metabolite-6488 50 0.0102 0.0308 54% 32684 Metabolite-11367 201 0.0105 0.0313 −37% 27738 threonate 50 0.0106 0.0216 −25% 1301 lysine 50 0.0107 0.0216 12% 33242 Metabolite-11897 201 0.011 0.0318 61% 9905 Metabolite-2231 61 0.0112 0.0223 17% 27288 Metabolite-10517 50 0.0114 0.0325 39% 1431 p-hydroxyphenyllactate-HPLA- 50 0.0115 0.0226 34% 25522 Metabolite-10407 50 0.0116 0.0226 25% 32515 valine* 200 0.0117 0.0327 10% 22154 bradykinin 200 0.0118 0.0327 1226% 32751 Metabolite-11434 201 0.0125 0.0341 33% 10247 Metabolite-2270 61 0.0127 0.0244 −68% 19397 Metabolite-6326 50 0.0132 0.0251 24% 15958 phenylacetate 201 0.0134 0.0362 −23% 15506 choline 61 0.0138 0.026 −3% 10737 Isobar-1-includes-mannose- 61 0.014 0.026 9% fructose-glucose-galactose- alpha-L-sorbopyranose-Inositol- D-allose-D--altrose-D-psicone- L--gulose-allo-inositol 587 gluconate 50 0.0142 0.0261 22% 33154 Metabolite-11809 200 0.0145 0.0387 −18% 12782 Metabolite-3100 50 0.0152 0.0276 64% 33227 Metabolite-11882 201 0.0154 0.0406 −41% 31787 3-carboxyl-4-methyl-5-propyl-2- 201 0.017 0.0436 −43% furanpropanoic acid 32197 3-(4-hydroxyphenyl)lactate 201 0.0178 0.0449 25% 9130 Metabolite-2139 61 0.0198 0.0355 26% 33087 peptide-RPPGFSPF 200 0.0212 0.0516 818% 32682 Metabolite-11365 201 0.0216 0.0521 −17% 32746 Metabolite-11429 201 0.0221 0.0526 18% 16337 Metabolite-4167 61 0.0222 0.0394 32% 5689 Metabolite-1111 61 0.0225 0.0395 66% 27727 glutathione, oxidized (GSSG) 200 0.0228 0.0536 −69% 11923 Metabolite-2821 61 0.025 0.0432 21% 14837 Metabolite-3707 61 0.0251 0.0432 −39% 13142 Metabolite-3165 61 0.0263 0.0446 14% 32769 Metabolite-11452 201 0.0267 0.0622 51% 606 uridine 61 0.0274 0.0461 −8% 18591 Metabolite-5646 61 0.0278 0.0463 −34% 10087 Metabolite-2249 61 0.029 0.0477 34% 32551 Metabolite-11234 201 0.0302 0.069 26% 33228 1-arachidonoylglycerophosphocholine 200 0.0303 0.069 −24% (Metabolite-11883) 12656 Metabolite-3025 50 0.0308 0.0502 13% 22261 Isobar-60-includes-s-2- 61 0.0312 0.0502 55% hydroxybutyric acid-2- hydroxyisobutyric acid 32405 3-indolepropionate 200 0.0313 0.0704 −36% 10156 Metabolite-2259 61 0.0314 0.0502 73% 7650 Metabolite-1834 61 0.0328 0.0518 47% 32741 Metabolite-11424 200 0.0329 0.0732 15% 32757 Metabolite-11440 201 0.0339 0.074 39% 33012 Metabolite-11674 200 0.0339 0.074 13% 12658 Metabolite-3026 50 0.0343 0.0537 15% 32110 Metabolite-11086 50 0.0344 0.0742 −14% 1284 threonine 50 0.0352 0.0546 −7% 32648 Metabolite-11331 201 0.0356 0.0758 −4% 32517 1-oleoylglycerophosphocholine 200 0.0359 0.0758 −17% (Metabolite-11203) 10092 Metabolite-2250 61 0.0363 0.0557 55% 63 cholesterol 50 0.0373 0.0561 12% 1507 palmitoleate 50 0.0376 0.0561 28% 12768 Metabolite-3088 50 0.0376 0.0561 −18% 32578 Metabolite-11261 200 0.0389 0.0806 26% 8796 Metabolite-2074 61 0.0391 0.0578 90% 33140 Metabolite-11795 200 0.04 0.082 24% 21127 1-palmitoylglycerol-1- 50 0.0424 0.0621 13% monopalmitin- 27256 Metabolite-10500 50 0.0432 0.0871 15% 13024 Metabolite-3139 61 0.0433 0.0628 33% 32599 Metabolite-11282 201 0.0434 0.0871 39% 30633 Metabolite-10785 61 0.044 0.0633 −22% 9491 Metabolite-2185 61 0.0445 0.0634 25% 32880 Metabolite-11563 201 0.0449 0.0893 −16% 594 nicotinamide 61 0.045 0.0636 −13% 12663 Metabolite-3030 50 0.0457 0.0638 −11% 30281 Metabolite-10743 50 0.047 0.0644 −17% 17390 Metabolite-4806 50 0.0478 0.0644 21% 12790 Metabolite-3108 50 0.048 0.0946 24% 19370 Metabolite-6268 50 0.048 0.0644 4% 6413 Metabolite-1342 61 0.0481 0.0644 −13% 18015 Metabolite-3113 61 0.0487 0.0644 −15% 17033 Metabolite-4613 61 0.049 0.0644 53% 12645 Metabolite-3017 50 0.0499 0.0644 −13% 17304 Metabolite-4759 61 0.0502 0.0644 26% 22895 Metabolite-9299 50 0.0503 0.0644 −9% 12647 Metabolite-3019 50 0.0513 0.0646 12% 16045 Metabolite-4006 50 0.0513 0.0646 26% 24077 Metabolite-9727 50 0.0519 0.0647 19% 27275 Metabolite-10507 50 0.0527 0.0653 −11% 32315 serine 50 0.0532 0.1028 −15% 31401 Metabolite-10892 50 0.0543 0.1029 36% 32452 propionylcarnitine 200 0.055 0.1029 18% 21631 Metabolite-8403 50 0.0552 0.1029 19% 32348 2-aminobutyrate 200 0.0561 0.1036 −13% 16019 Metabolite-3995 61 0.0584 0.0716 −24% 32776 2-methylbutyrylcarnitine 200 0.0584 0.1065 19% (Metabolite-11459) 10629 Metabolite-2386 61 0.0587 0.0716 26% 1572 Glycerate 50 0.0588 0.1065 −16% 12035 pelargonate-9-0- 50 0.0592 0.0716 −1% 31529 Metabolite-10941 61 0.0597 0.0717 18% 32762 Metabolite-11445 201 0.06 0.1078 −55% 32691 Metabolite-11374 200 0.0607 0.108 19% 528 alpha-keto-glutarate 61 0.0625 0.0744 28% 32792 Metabolite-11475 201 0.0631 0.1115 54% 32572 Metabolite-11255 200 0.0644 0.112 −51% 6398 Metabolite-1335 61 0.0645 0.0763 41% 27718 creatine 200 0.0652 0.112 17% 16666 Metabolite-4365 50 0.0656 0.112 −22% 18335 quinate 50 0.0656 0.112 −52% 12666 Metabolite-3033 50 0.0683 0.08 −11% 1366 trans-4-hydroxyproline 50 0.0699 0.0809 35% 32385 Metabolite-11180 50 0.0702 0.1172 −10% 18477 glycodeoxycholate 201 0.0704 0.1172 247% 10501 Metabolite-2321 61 0.0709 0.0815 −25% 32328 hexanoylcarnitine 200 0.0713 0.1177 17% 22133 DL-hexanoyl-carnitine 61 0.0719 0.082 28% 32596 Metabolite-02250 200 0.0722 0.1178 36% 32740 Metabolite-11423 201 0.0725 0.1178 13% 12785 Metabolite-3103 50 0.0743 0.1195 42% 32697 Metabolite-11380 200 0.0747 0.1195 13% 13557 Metabolite-3323 61 0.0779 0.0883 71% 32761 Metabolite-11444 201 0.0805 0.1278 31% 32952 Metabolite-02277 201 0.0819 0.1286 −75% 32945 Metabolite-11628 201 0.0822 0.1286 38% 32338 glycine 50 0.084 0.1292 −15% 1712 hydrocortisone 201 0.0854 0.1292 −19% 21418 Isobar-56-includes-DL-pipecolic 61 0.0856 0.0958 −24% acid-1-amino-1- cyclopentanecarboxylic acid 1105 linoleate 50 0.0858 0.0958 11% 32638 Metabolite-11321 201 0.0867 0.1296 −12% 12919 Metabolite-3130 61 0.0889 0.0978 67% 33203 Metabolite-11858 201 0.0893 0.1321 −63% 33366 Metabolite-12020 200 0.0896 0.1321 −27% 22842 cholate 201 0.0996 0.144 125% 32814 Metabolite-11497 201 0.1001 0.144 −7% 32743 bradykinin, hydroxyproline form- 200 0.1007 0.144 158% 32671 Metabolite-11354 200 0.1009 0.144 −13% 25459 Metabolite-10395 50 0.1012 0.144 −14% 12067 undecanoate 201 0.1035 0.1452 −5% 18868 Metabolite-5847 50 0.1035 0.1452 70% 21762 Metabolite-8506 61 0.1759 0.1569 −16%

TABLE 2 Biomarkers from subjects with Steatosis compared to Control subjects. % ID Biomarker Library p-value q-value Change 18706 Metabolite-5769 61 2.09E−06 0.0004 80% 12644 Metabolite-3016 50 2.08E−05 0.0021 30% 12751 Metabolite-3073 50 0.0001 0.0043 102% 24285 Metabolite-10026 61 0.0001 0.0031 64% 1125 isoleucine 50 0.0002 0.0076 48% 18882 Isobar-47-includes- 61 0.0003 0.0076 309% taurochenodeoxycholic acid- and-taurodeoxycholic acid 18476 glycocholate 61 0.0004 0.0092 248% 32322 glutamate 50 0.0005 0.0384 −91% 32497 10c-undecenoate 201 0.0005 0.0384 11% 31489 Metabolite-10914 50 0.0006 0.0384 47% 33073 cysteine-glutathione disulfide 200 0.0006 0.0384 53% (Metabolite-11728) 12658 Metabolite-3026 50 0.0007 0.0136 30% 33386 Metabolite-12035 50 0.0007 0.0384 −80% 584 mannose 50 0.001 0.0177 −25% 1299 tyrosine 200 0.0011 0.0499 −27% 17068 Metabolite-4627 61 0.0012 0.0203 263% 12780 Metabolite-3098 50 0.0015 0.0208 34% 60 leucine 50 0.0015 0.0208 −13% 18118 Metabolite-5346 50 0.0016 0.0208 42% 32393 glutamylvaline 200 0.0016 0.0577 −28% 2734 gamma-glutamyltyrosine 200 0.0018 0.0577 −24% 11923 Metabolite-2821 61 0.0019 0.0227 38% 27801 Metabolite-10589 61 0.002 0.0231 80% 32846 Metabolite-11529 201 0.002 0.0577 −172% 32701 urate- 200 0.0021 0.0577 −23% 10672 Metabolite-2390 61 0.0023 0.0245 57% 1649 valine 50 0.0024 0.0247 37% 32749 Metabolite-11432 201 0.003 0.0722 −106% 13142 Metabolite-3165 61 0.0031 0.029 25% 7650 Metabolite-1834 61 0.0035 0.029 144% 10715 Metabolite-2395 61 0.0035 0.029 661% 10551 Metabolite-2347 61 0.0036 0.029 154% 22133 DL-hexanoyl-carnitine 61 0.0036 0.029 317% 59 histidine 201 0.0036 0.0804 14% 16337 Metabolite-4167 61 0.0038 0.029 66% 22803 Isobar-66-includes- 61 0.0039 0.029 194% glycochenodeoxycholic acid- glycodeoxycholic acid 10737 Isobar-1-includes-mannose- 61 0.004 0.029 13% fructose-glucose-galactose- alpha-L-sorbopyranose- Inositol-D-allose-D--altrose-D- psicone-L--gulose-allo-inositol 25602 Metabolite-10432 50 0.0044 0.0867 −62% 12770 Metabolite-3090 50 0.0045 0.0867 −67% 9491 Metabolite-2185 61 0.0048 0.0338 50% 606 uridine 201 0.0049 0.0882 22% 12656 Metabolite-3025 50 0.0052 0.0354 22% 32776 2-methylbutyrylcarnitine 200 0.006 0.0975 −40% (Metabolite-11459) 21047 3-methyl-2-oxobutyric acid 61 0.0062 0.0409 20% 32748 Metabolite-11431 201 0.0062 0.0975 −82% 32641 Metabolite-11324 201 0.0065 0.0975 29% 18369 gamma-glutamylleucine 61 0.0065 0.0414 −18% 22600 Metabolite-9043 50 0.0068 0.0414 −51% 1126 alanine 50 0.0071 0.0414 26% 32412 butyrylcarnitine 200 0.0073 0.1038 −38% 16511 Metabolite-4274 50 0.0089 0.0489 39% 17304 Metabolite-4759 61 0.0094 0.0503 48% 17028 Metabolite-4611 50 0.0095 0.1279 −31% 32548 Metabolite-11231 201 0.0102 0.1281 −75% 13214 Metabolite-3183 61 0.0109 0.0524 28% 30821 Metabolite-10812 50 0.0112 0.0524 −20% 16016 Metabolite-3994 61 0.0113 0.0524 −45% 21762 Metabolite-8506 61 0.0118 0.0536 50% 33362 gamma- 200 0.0122 0.1281 −23% glutamylphenylalanine- 9130 Metabolite-2139 61 0.0126 0.0557 40% 13600 Metabolite-3330 61 0.013 0.0557 139% 31401 Metabolite-10892 50 0.0132 0.1281 −64% 32552 Metabolite-11235 201 0.0134 0.1281 −53% 64 phenylalanine 61 0.0134 0.0557 −9% 32564 Metabolite-11247 201 0.0135 0.1281 62% 19490 Metabolite-6488 50 0.0139 0.1281 −47% 32578 Metabolite-11261 200 0.0144 0.1281 −54% 16308 Metabolite-4147 50 0.0145 0.1281 −48% 32754 Metabolite-11437 201 0.0148 0.1281 50% 32547 Metabolite-11230 201 0.0151 0.1281 −83% 32808 Metabolite-11491 201 0.0154 0.1281 −86% 12783 Metabolite-3101 50 0.0158 0.1281 −39% 1769 cortisone 201 0.0162 0.1281 28% 32945 Metabolite-11628 201 0.0179 0.1339 −68% 2761 thyroxine 61 0.018 0.0734 31% 12647 Metabolite-3019 50 0.0184 0.0734 17% 12763 Metabolite-3083 50 0.0186 0.1339 −36% 32881 Metabolite-11564 201 0.0186 0.1339 −26% 15500 carnitine 200 0.0189 0.1339 −11% 16865 Metabolite-4522 50 0.0192 0.075 22% 1494 5-oxoproline 50 0.0197 0.075 20% 30805 Metabolite-10810 50 0.0198 0.1349 −75% 32753 Metabolite-09789 201 0.0201 0.1349 51% 1642 caprate-10-0- 201 0.0219 0.1425 7% 32854 Metabolite-11537 200 0.0223 0.1425 46% 32863 Metabolite-11546 201 0.0257 0.1608 −116% 9905 Metabolite-2231 61 0.0262 0.0921 18% 12790 Metabolite-3108 50 0.0276 0.1669 −26% 527 lactate 50 0.0276 0.0952 −20% 32926 Metabolite-11609 201 0.028 0.1669 −44% 8509 Metabolite-2041 61 0.0298 0.0996 22% 8210 Metabolite-1981 61 0.0309 0.1016 −35% 32519 1- 200 0.0312 0.1764 22% linoleoylglycerophosphocholine (Metabolite-11205) 32813 Metabolite-11496 201 0.0347 0.1875 16% 32698 Metabolite-11381_200 200 0.036 0.1875 −22% 32631 Metabolite-11314 200 0.0365 0.1875 −11% 32517 1-oleoylglycerophosphocholine 200 0.037 0.1875 26% (Metabolite-11203) 32807 Metabolite-11490 201 0.04 0.1972 −125% 18497 taurocholate 201 0.0404 0.1972 −115% 15140 L-kynurenine 61 0.0407 0.1275 24% 12782 Metabolite-3100 50 0.0422 0.1978 −49% 27727 glutathione, oxidized (GSSG) 200 0.0426 0.1978 72% 32621 Metabolite-11304 200 0.0427 0.1978 −43% 13557 Metabolite-3323 61 0.0447 0.135 68% 31595 Metabolite-10951 61 0.0451 0.135 27% 20699 erythritol 50 0.0452 0.2057 −23% 32878 Metabolite-11561 201 0.0477 0.2137 −32% 1113 isocitrate 61 0.0497 0.144 34% 32910 Metabolite-11593 201 0.0507 0.2233 −20% 17390 Metabolite-4806 50 0.0519 0.1462 27% 32752 Metabolite-11435 201 0.0533 0.2263 −41% 1647 glutamine 50 0.0534 0.147 21% 33242 Metabolite-11897 201 0.0544 0.2263 −38% 32452 propionylcarnitine 200 0.0545 0.2263 −27% 32978 Metabolite-11656 200 0.0547 0.2263 6% 27710 N-acetylglycine 50 0.0581 0.155 −29% 12785 Metabolite-3103 50 0.0581 0.2331 −48% 33228 1-arachidonoylglycerophospho- 200 0.0581 0.2331 18% choline (Metabolite-11883) 1336 palmitate 50 0.0586 0.155 14% 594 nicotinamide 200 0.0598 0.2337 16% 15996 aspartate 50 0.0606 0.2337 −29% 10245 Metabolite-2269- 61 0.0622 0.1602 −42% 33131 Metabolite-11786 200 0.0626 0.2367 33% 32559 Metabolite-11242 201 0.0641 0.2372 −27% 15529 Metabolite-3951 61 0.0658 0.1647 22% 5687 Metabolite-1110 61 0.0702 0.1672 −43% 19370 Metabolite-6268 50 0.0707 0.1672 25% 32549 Metabolite-02269 201 0.0733 0.2653 44% 12789 Metabolite-3107 50 0.0741 0.2653 −59% 528 alpha-keto-glutarate 61 0.0742 0.1717 −27% 31510 Metabolite-10932 61 0.0764 0.1746 21% 19985 Metabolite-6957 50 0.0799 0.2678 −20% 10247 Metabolite-2270 61 0.0802 0.1773 −64% 18868 Metabolite-5847 50 0.0804 0.2678 −67% 32675 Metabolite-03951_200 200 0.0812 0.2678 −15% 20488 glucose 50 0.0819 0.1773 11% 18335 quinate 50 0.0822 0.2678 20% 15676 3-methyl-2-oxovalerate 201 0.0827 0.2678 −17% 25601 Metabolite-10431 50 0.0828 0.2678 −41% 12593 Metabolite-2973 50 0.0852 0.2678 −22% 32838 Metabolite-11521 200 0.0856 0.2678 −35% 19397 Metabolite-6326 50 0.086 0.1815 19% 31787 3-carboxyl-4-methyl-5-propyl- 201 0.0861 0.2678 53% 2-furanpropanoic acid 12604 Metabolite-2981 50 0.0864 0.2678 −15% 21631 Metabolite-8403 50 0.0885 0.2678 −26% 1515 salicylic acid 50 0.0887 0.2678 −140% 1301 lysine 200 0.09 0.2687 −14% 32511 EDTA* 201 0.0942 0.2759 −7% 32971 Metabolite-11654 200 0.0945 0.2759 −36% 12767 Metabolite-3087 50 0.096 0.1954 35% 32795 Metabolite-11478 201 0.0982 0.2796 −35% 32793 Metabolite-11476 200 0.0988 0.2796 6%

TABLE 3 Biomarkers from subjects with Steatosis compared to Steatohepatitis subjects. Li- ID Biomarker brary p-value q-value % Change 21762 Metabolite-8506 61 0.0006 0.1449 −44% 528 alpha-keto-glutarate 61 0.0017 0.1921 75% 6847 Metabolite-1496 61 0.0025 0.1921 49% 16650 Metabolite-4360 50 0.0033 0.1921 −144% 18591 Metabolite-5646 61 0.0041 0.1921 −53% 27718 creatine 200 0.0071 1 −50% 599 pyruvate 61 0.0087 0.253 −42% 32621 Metabolite-11304 200 0.0124 1 31% 1113 isocitrate 61 0.0151 0.3249 38% 15506 choline 61 0.0163 0.3249 −28% 32729 Metabolite-11412 200 0.0166 1 −14% 10501 Metabolite-2321 61 0.0183 0.3249 −40% 12644 Metabolite-3016 50 0.0186 0.3249 −13% 32735 Metabolite-01911 200 0.0199 1 −99% 15753 hippurate 201 0.0255 1 44% 30805 Metabolite-10810 50 0.0302 1 37% 12768 Metabolite-3088 50 0.0305 0.4178 −25% 10629 Metabolite-2386 61 0.0335 0.4178 25% 33209 Metabolite-11864 201 0.0376 1 45% 32855 Metabolite-11538 201 0.0429 1 34% 32416 alpha linolenate (18:3(n- 201 0.0486 1 32% 3)) 16518 Metabolite-4276 50 0.0499 0.481 63% 20267 Metabolite-7187 61 0.0521 0.481 37% 19462 Metabolite-6446 50 0.0552 0.481 22% 33420 gamma-tocopherol- 50 0.0556 1 −54% 1515 salicylic acid 50 0.0569 1 60% 32567 Metabolite-11250 200 0.0573 1 40% 32769 Metabolite-11452 201 0.0608 1 −79% 12774 Metabolite-3094 50 0.0625 1 −24% 32632 Metabolite-11315 200 0.0651 1 21% 32741 Metabolite-11424 200 0.0655 1 −13% 12658 Metabolite-3026 50 0.0658 0.525 −12% 32110 Metabolite-11086 50 0.0659 1 17% 30728 Metabolite-10797 61 0.0693 0.525 65% 32625 Metabolite-11308 201 0.0697 1 27% 15140 kynurenine 200 0.0702 1 −14% 12645 Metabolite-3017 50 0.0709 0.525 −22% 33154 Metabolite-11809 200 0.0711 1 19% 12067 undecanoate 201 0.072 1 6% 5628 Metabolite-1086 61 0.0723 0.525 45% 19402 Metabolite-6346 50 0.077 1 −20% 32631 Metabolite-11314 200 0.0779 1 8% 7933 Metabolite-1911 61 0.0792 0.5308 75% 32797 Metabolite-11480 201 0.0818 1 −213% 18254 paraxanthine 200 0.0824 1 −69% 32912 Metabolite-11595 201 0.0834 1 25% 32717 Metabolite-11400 200 0.0855 1 39% 33133 Metabolite-11788 200 0.0862 1 −11% 22261 Isobar-60-includes-s-2- 61 0.0869 0.5308 42% hydroxybutyric acid-2- hydroxyisobutyric acid 19374 Metabolite-6270 50 0.09 1 27% 6398 Metabolite-1335 61 0.0908 0.5308 28% 12770 Metabolite-3090 50 0.0919 1 28% 18392 theobromine 200 0.0956 1 −118% 32978 Metabolite-11656 200 0.0961 1 −6% 32761 Metabolite-11444 201 0.0962 1 −11% 31548 peptide- 200 0.0981 1 −323% DSGEGDFXAEGGGVR 3141 betaine 200 0.0986 1 14% 606 uridine 201 0.0987 1 −17% 24233 Metabolite-9855 61 0.0996 0.5308 99%

Example 2 Random Forest Classification of Subjects

Random forest analyses were used for classification of samples into groups (e.g. disease or healthy, steatosis or healthy, steatohepatitis or healthy, steatosis or NASH). Random forests give an estimate of how well we can classify individuals in a new data set into each group, in contrast to a t-test, which tests whether the unknown means for two populations are different or not. Random forests create a set of classification trees based on continual sampling of the experimental units and compounds. Then each observation is classified based on the majority votes from all the classification trees.

Random forest results show that the samples can be classified correctly with varying degrees of accuracy using the biomarkers listed in Tables 1, 2, and/or 3. The confusion matrices demonstrate that using plasma samples steatosis subjects, steatohepatitis subjects and control subjects can be distinguished (Table 4), steatosis subjects can be distinguished from control subjects (Table 5), steatohepatitis subjects can be distinguished from control subjects (Table 6) and steatosis subjects can be distinguished from steatohepatitis subjects (Table 7). The “Out-of-Bag” (OOB) Error rate gives an estimate of how accurately new observations can be predicted using the random forest model (e.g., whether a sample is from a subject having steatosis or a control subject).

TABLE 4 Confusion Matrix showing Control subjects, Steatosis subjects and NASH subjects can be distinguished from each other. Confusion Matrix: Control vs. Steatosis vs. NASH Control_Predicted Steatosis_Predicted NASH_Predicted Error Control 23 0 2 8% Steatosis 3 9 12 63% NASH 2 4 5 55% Out_of_bag_error 38%

Based on the OOB Error rate of 38%, the Random Forest model that was created could be used to predict whether a subject has steatosis, steatohepatitis, or no fatty liver disease with about 62% accuracy from analysis of the levels of the biomarkers in samples from the subject. The biomarkers that are the most important biomarkers for distinguishing the groups are listed in Table 4B and displayed in FIG. 3.

TABLE 4B Most important biomarkers to distinguish control, steatosis and steatohepatitis subjects from Random Forest Importance Plot shown in FIG. 3. ID Biomarker 2734 gamma-glutamyltyrosine 32322 glutamate 33073 cysteine-glutathione disulfide (Metabolite-11728) 32412 butyrylcarnitine 18497 taurocholate 32393 glutamylvaline 1299 tyrosine 32739 Metabolite-11422 15140 kynurenine 32868 glycocholate* 33420 gamma-tocopherol- 32748 Metabolite-11431 27718 creatine 31489 Metabolite-10914 32552 Metabolite-11235 15753 hippurate 32846 Metabolite-11529 17028 Metabolite-4611 32735 Metabolite-01911_200 32808 Metabolite-11491 15990 glycerophosphorylcholine (GPC) 32701 urate- 33362 gamma-glutamylphenylalanine- 32547 Metabolite-11230 32497 10c-undecenoate 32632 Metabolite-11315 59 histidine 32621 Metabolite-11304 32749 Metabolite-11432 32559 Metabolite-11242 1126 alanine 1113 isocitrate 1125 isoleucine 527 lactate 60 leucine 584 mannose 24285 Metabolite-10026 30821 Metabolite-10812 6847 Metabolite-1496 7933 Metabolite-1911 10715 Metabolite-2395 12644 Metabolite-3016 12658 Metabolite-3026 12751 Metabolite-3073 12780 Metabolite-3098 13600 Metabolite-3330 16511 Metabolite-4274 18706 Metabolite-5769 20267 Metabolite-7187 21762 Metabolite-8506 24233 Metabolite-9855 18392 theobromine 2761 thryoxine 1604 urate 1649 valine 3147 xanthine

TABLE 5 Confusion Matrix showing Control subjects can be distinguished from Steatosis subjects. Confusion Matrix: Control vs. Steatosis Control_Predicted Steatosis_Predicted Error Control 22 3 12% Steatosis 1 10 9% Out_of_bag_error 11%

Based on the OOB Error rate of 11%, the Random Forest model that was created could be used to predict whether a subject has steatosis or no fatty liver disease with about 89% accuracy from analysis of the levels of the biomarkers in samples from the subject. The biomarkers that are the most important biomarkers for distinguishing the groups are listed in Table 5B and displayed in FIG. 1.

TABLE 5B Most important biomarkers from Random Forest Importance Plot shown in FIG. 1. ID Biomarker 2734 gamma-glutamyltyrosine 18497 taurocholate 32412 butyrylcarnitine 32552 Metabolite-11235 1299 tyrosine 606 uridine 32322 glutamate 32621 Metabolite-11304 17028 Metabolite-4611 19490 Metabolite-6488 32497 10c-undecenoate 32748 Metabolite-11431 12789 Metabolite-3107 32393 glutamylvaline 32547 Metabolite-11230 12763 Metabolite-3083 32808 Metabolite-11491 25602 Metabolite-10432 32739 Metabolite-11422 33073 cysteine-glutathione disulfide (Metabolite-11728) 31489 Metabolite-10914 32749 Metabolite-11432 32631 Metabolite-11314 33386 Metabolite-12035 32559 Metabolite-11242 32846 Metabolite-11529 33242 Metabolite-11897 33362 gamma-glutamylphenylalanine- 32945 Metabolite-11628 12790 Metabolite-3108 1126 alanine 18476 glycocholate Isobar 47 (taurochenodeoxycholic acid, taurodeoxycholic acid) Isobar 66 (glycochenodeoxycholic acid, glychodeoxycholic acid) 57 lactate 584 mannose 24285 Metabolite-10026 31595 Metabolite-10951 10551 Metabolite-2347 11923 Metabolite-2821 12644 Metabolite-3016 12647 Metabolite-3019 12656 Metabolite-3025 12658 Metabolite-3026 12751 Metabolite-3073 Metabolite-3077 12780 Metabolite-3098 13142 Metabolite-3165 13600 Metabolite-3330 16337 Metabolite-4167 Metabolite-4361 17304 Metabolite-4759 17390 Metabolite-4806 18118 Metabolite-5346 18706 Metabolite-5769 21762 Metabolite-8506 2761 thyroxine 1604 urate

TABLE 6 Confusion Matrix showing Control subjects can be distinguished from NASH subjects. Confusion Matrix: Control vs. NASH Control_Predicted NASH_Predicted Error Control 23 2 8% NASH 2 22 8% Out_of_bag_error 8%

Based on the OOB Error rate of 8%, the Random Forest model that was created could be used to predict whether a subject has steatohepatitis or no fatty liver disease with about 92% accuracy from analysis of the levels of the biomarkers in samples from the subject. The biomarkers that are the most important biomarkers for distinguishing the groups are listed in Table 6B and displayed in the importance plot in FIG. 2.

TABLE 6B Most important biomarkers from Random Forest Importance Plot shown in FIG. 2. ID Biomarker 33073 cysteine-glutathione disulfide (Metabolite-11728) 15140 kynurenine 32868 glycocholate* 32412 butyrylcarnitine 32322 glutamate 2734 gamma-glutamyltyrosine 15753 hippurate 31489 Metabolite-10914 32739 Metabolite-11422 33420 gamma-tocopherol- 32881 Metabolite-11564 32393 glutamylvaline 32519 1-linoleoylglycerophosphocholine (Metabolite-11205) 32748 Metabolite-11431 32701 urate- 32550 Metabolite-02272_201 1299 tyrosine 32548 Metabolite-11231 33362 gamma-glutamylphenylalanine- 584 mannose 32675 Metabolite-03951_200 32632 Metabolite-11315 32846 Metabolite-11529 17028 Metabolite-4611 32497 10c-undecenoate 32749 Metabolite-11432 16308 Metabolite-4147 32544 Metabolite-11227 18497 taurocholate 32697 Metabolite-11380 21047 3-methyl-2-oxobutyric acid 1126 alanine 1647 glutamine 1113 isocitrate 1125 isoleucine 60 leucine 20699 meso-erythritol 24285 Metabolite-10026 30821 Metabolite-10812 5628 Metabolite-1086 5687 Metabolite-1110 6398 Metabolite-1335 6847 Metabolite-1496 8509 Metabolite-2041 10286 Metabolite-2272 10715 Metabolite-2395 12751 Metabolite-3073 12767 Metabolite-3087 12780 Metabolite-3098 16511 Metabolite-4274 18706 Metabolite-5769 20267 Metabolite-7187 1649 valine 3147 xanthine

TABLE 7 Confusion Matrix showing Steatosis subjects can be distinguished from steatohepatitis (NASH) subjects. Confusion Matrix: Steatosis vs. NASH Steatosis_Predicted NASH_Predicted Error Steatosis 10 14 58% NASH 5 6 45% Out_of_bag_error 54%

Based on the OOB Error rate of 54%, the Random Forest model that was created could be used to predict whether a subject has steatosis or steatohepatitis with about 46% accuracy from analysis of the levels of the biomarkers in samples from the subject.

The compounds that are the most important biomarkers for distinguishing the groups are shown in the importance plots in FIGS. 1, 2, 3. FIG. 1 lists the biomarker compounds that are most important in distinguishing steatosis subjects and Control subjects. Listed in FIG. 2 are the biomarker compounds that are most important in distinguishing steatohepatitis subjects from control subjects. Listed in FIG. 3 are the biomarker compounds that are most important in distinguishing steatosis, steatohepatitis and control subjects.

Example 3 Recursive Partitioning Classification and Receiver Operator Curves (ROC)

Recursive partitioning was performed to uncover the biomarkers that can best differentiate the 2 groups of subjects. It relates a ‘dependent’ variable (e.g. Group or Y) to a collection of independent (‘predictor’) variables (e.g. metabolites or X) in order to uncover—or simply understand—the elusive relationship, Y=f(X). It was performed with the JMP program (SAS) to generate a decision tree. The significance level of each “split” of data into the nodes or branches of the tree was computed as p-values, which discern the quality of the split relative to a random event. It was given as LogWorth, which is the negative log 10 of a raw p-value.

The recursive partitioning results showing the classification of Control and steatosis subjects is shown in FIG. 4. The biomarkers that contribute to the classification are listed in Table 8. The recursive partitioning results showing the classification of steatosis and steatohepatitis (NASH, SH) are shown in FIG. 5 and the biomarkers used in the classification are listed in Table 9. The recursive partitioning results showing the classification of control and steatohepatitis are shown in FIG. 6 and the biomarkers are listed in Table 10. The recursive partitioning results showing the use of the biomarkers to classify steatosis, steatohepatitis and control subjects are shown in FIG. 7 and the biomarkers are listed in Table 11.

The sensitivity and specificity of a given biomarker was determined by the Receiver Operating Characteristic curve (or ROC curve), which plots the true positive rate against the false positive rate for the different possible cutpoints of the specific biomarker. The area under the curve is a measure of test accuracy. An area of 1 represents a perfect test while an area of 0.5 represents a worthless test. A rough guide for classifying the accuracy of a diagnostic test is the traditional academic point system:

0.90-1=excellent

0.80-0.90=good

0.70-0.80=fair

0.60-0.70=poor

0.50-0.60=fail

ROC curves are shown in FIGS. 4, 5, 6 and 7. The control subjects are separated from steatosis subjects with an accuracy of >90 (FIG. 4). The steatosis and steatohepatitis subjects can be distinguished with >82% accuracy (FIG. 5). The Control and steatohepatitis subjects are classified >87% accuracy (FIG. 6). The three groups can be classified using the biomarkers with >70% (NASH), >77% (Steatosis), >86% accuracy (control) (FIG. 7).

TABLE 8 Recursive Partitioning Biomarkers, Steatosis vs control. Candidate ID COMPOUND Library LogWorth G{circumflex over ( )}2 2734 gamma glutamyltyrosine 200 7.2110 29.7676 18497 taurocholate 201 4.5487 20.2589 32599 Metabolite-11282 201 3.4916 17.2822 20699 erythritol 50 3.4916 17.2822 32552 Metabolite-11235 201 3.3649 16.7903 32322 glutamate 50 3.3649 16.7903 32393 glutamylvaline 200 3.0618 15.2513 12763 Metabolite-3083 50 3.0147 15.2513 32497 10c undecenoate 201 2.9895 15.3210 19490 Metabolite-6488 50 2.9743 15.2513 1299 tyrosine 200 2.9718 15.2513 33242 Metabolite-11897 201 2.9718 15.2513 32748 Metabolite-11431 201 2.9601 15.2052 32621 Metabolite-11304 200 2.8266 14.6774 606 uridine 201 2.8266 14.6774 17028 Metabolite-4611 50 2.8266 14.6774 12789 Metabolite-3107 50 2.4680 12.6627 32808 Metabolite-11491 201 2.3944 12.6627 32412 butyrylcarnitine 200 2.3372 12.6627 32547 Metabolite-11230 201 2.3242 12.6627 12790 Metabolite-3108 50 2.2779 12.4746 33073 cysteine-glutathione 200 2.2201 12.1291 disulfide (Metabolite- 11728) 32739 Metabolite-11422 201 2.1932 12.1291 30805 Metabolite-10810 50 2.1932 12.1291 32776 2-methylbutyrylcarnitine 200 2.1488 11.9121 (Metabolite-11459) 32697 Metabolite-11380 200 2.1443 11.9121 32701 urate 200 2.1403 11.9121 32559 Metabolite-11242 201 2.1279 11.8611 32631 Metabolite-11314 200 2.1279 11.8611 32945 Metabolite-11628 201 2.1279 11.8611 33386 Metabolite-12035 50 2.0266 11.2117

TABLE 9 RP Biomarker Candidates, steatosis vs. steatohepatitis. Candidate ID COMPOUND Library LogWorth G{circumflex over ( )}2 27718 creatine 200 2.8020 14.5688 12067 undecanoate 201 2.7871 14.5095 32729 Metabolite-11412 200 2.5472 13.5499 32855 Metabolite-11538 201 2.5472 13.5499 32735 Metabolite-01911_200 200 2.4708 13.0756 32621 Metabolite-11304 200 2.1940 12.1162 27275 Metabolite-10507 50 2.0579 11.5561

TABLE 10 RP Biomarkers of steatohepatitis (NASH) vs Control. Li- Candidate ID COMPOUND brary LogWorth G{circumflex over ( )}2 33073 cysteine-glutathione disulfide 200 7.3726 31.8519 (Metabolite-11728) 32868 glycocholate 201 6.5169 28.4230 15140 kynurenine 200 6.4082 27.7714 2734 gamma glutamyltyrosine 200 5.5940 24.5076 32412 butyrylcarnitine 200 5.4888 24.6395 15753 hippurate 201 5.2301 23.5921 32322 glutamate 50 4.5652 21.1976 33420 gamma tocopherol 50 4.5330 21.1976 32748 Metabolite-11431 201 4.3254 20.3263 32739 Metabolite-11422 201 4.2214 19.9467 32393 glutamylvaline 200 3.9888 19.1924 32550 Metabolite-02272 201 3.9860 19.0839 31489 Metabolite-10914 50 3.8423 18.5637 32519 1- 200 3.8117 18.4417 linoleoylglycerophosphocholine (Metabolite-11205) 33133 Metabolite-11788 200 3.6919 17.9988 32632 Metabolite-11315 200 3.6913 17.9988 32548 Metabolite-11231 201 3.6260 17.7540 16308 Metabolite-4147 50 3.6255 17.7540 32881 Metabolite-11564 201 3.5761 17.5685 32675 Metabolite-03951 200 3.5752 17.5685 32697 Metabolite-11380 200 3.4581 17.1290 32863 Metabolite-11546 201 3.4182 16.9876 17028 Metabolite-4611 50 3.3802 16.8375 584 mannose 50 3.3005 16.5389 33362 gamma glutamylphenylalanine 200 3.1967 16.1427 32749 Metabolite-11432 201 3.1952 16.1427 32701 urate 200 3.1486 15.9669 32846 Metabolite-11529 201 3.1224 15.8433 1299 tyrosine 200 3.1159 15.8433 32544 Metabolite-11227 201 3.1019 15.7905 32564 Metabolite-11247 201 2.9040 15.0281 32517 1-oleoylglycerophosphocholine 200 2.9012 15.0281 (Metabolite-11203) 32808 Metabolite-11491 201 2.8993 14.9830 33012 Metabolite-11674 200 2.8909 14.9830 31787 3-carboxyl-4-methyl-5-propyl- 201 2.8894 14.9830 2-furanpropanoic acid 20699 erythritol 50 2.8894 14.9830 32110 Metabolite-11086 50 2.8894 14.9830 12783 Metabolite-3101 50 2.8894 14.9830 19402 Metabolite-6346 50 2.8894 14.9830 18497 taurocholate 201 2.6891 13.9546 59 histidine 201 2.6508 14.0671 12774 Metabolite-3094 50 2.6508 14.0671 32682 Metabolite-11365 201 2.6217 13.9546 33227 Metabolite-11882 201 2.5727 13.7651 15990 glycerophosphorylcholine 200 2.5636 13.7298 (GPC) 32547 Metabolite-11230 201 2.5131 13.5334 32497 X10c undecanoate 201 2.4846 13.4226 32549 Metabolite-02269 201 2.4738 13.3217 32552 Metabolite-11235 201 2.4690 13.3619 32385 Metabolite-11180 50 2.4078 13.0513 32621 Metabolite-11304 200 2.3922 13.0617 20488 glucose 50 2.3922 13.0617 16865 Metabolite-4522 50 2.3036 12.7022 64 phenylalanine 200 2.2026 12.3145 32637 Metabolite-11320 201 2.1916 12.2709 32813 Metabolite-11496 201 2.1916 12.2709 32854 Metabolite-11537 200 2.1916 12.2709 27256 Metabolite-10500 50 2.1916 12.2709 32346 glycochenodeoxycholate 201 2.1443 11.9681 32656 Metabolite-11339 201 2.0482 11.6993 16650 Metabolite-4360 50 2.0204 11.5103 607 urocanate 200 2.0135 10.2237 32559 Metabolite-11242 201 2.0012 11.5103

TABLE 11 RP Biomarkers of Steatosis vs Steatohepatitis (NASH) Vs Control. Li- Candidate ID COMPOUND brary LogWorth G{circumflex over ( )}2 2734 gamma glutamyltyrosine 200 8.7610 39.6669 33073 cysteine-glutathione disulfide 200 6.9137 33.1250 (Metabolite-11728) 32868 glycocholate 201 5.6594 28.4476 32412 butyrylcarnitine 200 5.5221 27.9670 15140 kynurenine 200 5.4648 27.7794 32322 glutamate 50 5.4202 27.6254 18497 taurocholate 201 4.8465 25.6695 32748 Metabolite-11431 201 4.5458 24.5681 32393 glutamylvaline 200 4.4325 24.1102 1299 tyrosine 200 4.4170 24.1102 15753 hippurate 201 4.2727 23.5947 32739 Metabolite-11422 201 3.9941 22.5914 17028 Metabolite-4611 50 3.7736 21.7885 33420 gamma tocopherol 50 3.6961 21.4058 20699 erythritol 50 3.4424 20.5670 31489 Metabolite-10914 50 3.4147 20.4362 32552 Metabolite-11235 201 3.4135 20.4592 32697 Metabolite-11380 200 3.2207 19.7221 32519 1- 200 3.2148 19.7148 linoleoylglycerophosphocholine (Metabolite-11205) 32749 Metabolite-11432 201 3.1735 19.5592 33362 gamma glutamylphenylalanine 200 3.1317 19.2619 32846 Metabolite-11529 201 3.1175 19.1830 32550 Metabolite-02272_201 201 3.0781 19.1976 32808 Metabolite-11491 201 2.9873 18.6852 32547 Metabolite-11230 201 2.9438 18.6852 16308 Metabolite-4147 50 2.8966 18.4875 32548 Metabolite-11231 201 2.8923 18.4875 33133 Metabolite-11788 200 2.8602 18.3636 32735 Metabolite-01911_200 200 2.8564 18.1394 32863 Metabolite-11546 201 2.8379 18.1709 32881 Metabolite-11564 201 2.8103 18.1709 33242 Metabolite-11897 201 2.8094 18.1676 32675 Metabolite-03951_200 200 2.7808 18.0072 59 histidine 201 2.7802 18.0542 32632 Metabolite-11315 200 2.7704 17.9993 27718 creatine 200 2.6480 17.5391 32701 urate 200 2.6462 17.5321 584 mannose 50 2.6462 17.5321 32599 Metabolite-11282 201 2.5826 17.2822 32559 Metabolite-11242 201 2.5799 17.2717 32497 10c undecenoate 201 2.5001 16.9560 606 uridine 201 2.4079 16.5890 32621 Metabolite-11304 200 2.4027 16.5683 12783 Metabolite-3101 50 2.4027 16.5683 32564 Metabolite-11247 201 2.3935 16.4772 32517 1-oleoylglycerophosphocholine 200 2.3800 16.4772 (Metabolite-11203) 12763 Metabolite-3083 50 2.3715 16.2338 32544 Metabolite-11227 201 2.3539 16.3727 19490 Metabolite-6488 50 2.3240 16.2338 12067 undecanoate 201 2.3176 16.2264 32854 Metabolite-11537 200 2.2206 15.7775 32813 Metabolite-11496 201 2.2068 15.7775 32945 Metabolite-11628 201 2.1937 15.7238 32110 Metabolite-11086 50 2.1659 15.6103 19402 Metabolite-6346 50 2.1659 15.6103 31787 3-carboxyl-4-methyl-5-propyl-2- 201 2.1292 15.4023 furanpropanoic acid 32729 Metabolite-11412 200 2.1279 15.4545 32549 Metabolite-02269 201 2.0837 15.0750 15990 glycerophosphorylcholine GPC 200 2.0516 15.1396 33012 Metabolite-11674 200 2.0280 14.9832 32385 Metabolite-11180 50 2.0268 14.8693

Example 4 Biomarkers and Gender

Biomarkers were discovered by (1) analyzing blood plasma samples from different groups of female human subjects and from different groups of male human subjects to determine the levels of metabolites in the samples and then (2) statistically analyzing the results to determine those metabolites that were differentially present in the two groups.

For males, the plasma samples used for the analysis were 7 control samples that were from healthy subjects, 6 samples from patients with steatosis and 8 samples from patients with steatohepatitis. For females, the plasma samples used for the analysis were 18 control samples that were from healthy subjects, 5 samples from patients with steatosis and 16 samples from patients with steatohepatitis. In both the male and the female studies, after the levels of metabolites were determined, the data was analyzed using significance tests (ANOVA, ANCOVA, Wilcoxon).

ANOVA was used to identify significant differences in the mean levels of metabolites between two populations (i.e., Steatosis vs. Control, Steatohepatitis vs. Control, Steatosis vs. Steatohepatitis).

Biomarkers:

As listed below in Tables 12 (male subjects) and 13 (female subjects), biomarkers were discovered that were differentially present between plasma samples from steatosis patients and Control subjects, biomarkers that were discovered that were differentially present between plasma samples from patients with steatohepatitis and from Control subjects and biomarkers that were discovered that were differentially present between plasma samples from steatosis and plasma samples from subjects with steatohepatitis (i.e. NASH).

Tables 12 and 13 include, for each listed biomarker, the p-value and the q-value determined in the statistical analysis of the data concerning the biomarkers in the steatosis mean level as compared to the control mean level, the NASH mean level as compared to the control mean level, and the steatosis mean level as compared to the NASH mean level. Library indicates the chemical library that was used to identify the compounds. The number 50 refer to the GC library and the number 61 refers to the LC library. Comp ID refers to the identification number for the compound in our internal chemical compound database.

Table 12 lists biomarkers for males, including, for each biomarker, the p-value and q-value for the specified comparisons of groups as described above.

TABLE 12 Biomarkers from male subjects with Steatosis compared to Control subjects, steatohepatitis (NASH) compared to Control subjects and Steatosis compared to steatohepatitis (NASH) subjects. Control vs. NASH vs. Control vs. NASH STEATOSIS STEATOSIS COMPOUND COMP_ID LIBRARY p-value q-value p-value q-value p-value q-value stearate 1358 50 4.00E−04 0.0633 0.0128 0.2286 0.3457 0.7893 palmitate 1336 50 9.00E−04 0.0633 0.0091 0.1836 0.5612 0.8289 isoleucine 1125 50 0.0011 0.0633 0.0025 0.1264 0.9729 0.8893 glutamate 57 50 0.0011 0.0633 0.0305 0.2368 0.3124 0.7803 Metabolite-9033 22570 50 0.0021 0.0872 0.2901 0.66 0.0507 0.6206 Metabolite-3087 12767 50 0.0031 0.0872 0.1491 0.4771 0.1507 0.7628 valine 1649 50 0.0034 0.0872 0.0243 0.2368 0.5868 0.832 isoleucine 18118 50 0.0036 0.0872 0.0019 0.1264 0.6472 0.8475 glutamate 12751 50 0.0036 0.0872 0.0472 0.2804 0.4112 0.8022 Metabolite-4274 16511 50 0.0038 0.0872 0.106 0.3961 0.2349 0.7803 linoleate 1105 50 0.0049 0.0872 0.051 0.2862 0.4508 0.8145 leucine 60 50 0.0054 0.0872 0.0175 0.2368 0.8025 0.8527 Metabolite-5769 18706 61 0.0054 0.0872 0.0025 0.1264 0.626 0.8421 isocitrate 1113 61 0.0055 0.0872 0.094 0.3845 0.3151 0.7803 Metabolite-2041 8509 61 0.0059 0.0872 0.0536 0.2922 0.4791 0.8182 Metabolite-2270 10247 61 0.0061 0.0872 0.0206 0.2368 0.7827 0.8519 Metabolite-10932 31510 61 0.007 0.0872 0.0338 0.2455 0.6578 0.8475 alanine 1126 50 0.0072 0.0872 0.0298 0.2368 0.7063 0.8475 tyrosine 12780 50 0.0073 0.0872 0.0272 0.2368 0.7393 0.8519 Metabolite-8214 21421 50 0.0085 0.0948 0.0925 0.3845 0.398 0.8022 glucose 20488 50 0.0095 0.0948 0.0911 0.3845 0.4236 0.8022 Metabolite-10439 25609 50 0.0098 0.0948 0.0341 0.2455 0.7445 0.8519 Metabolite-1496 6847 61 0.0098 0.0948 0.9827 0.8655 0.0124 0.348 choline 15506 61 0.01 0.0948 0.6398 0.7767 0.0429 0.6206 glutamine 1647 50 0.0116 0.1058 0.1325 0.4409 0.3566 0.7893 Metabolite-9855 24233 61 0.0121 0.1061 0.8754 0.8551 0.0105 0.3457 Metabolite-7187 20267 61 0.0145 0.1218 0.4129 0.698 0.1218 0.7385 Metabolite-5646 18591 61 0.0157 0.1272 0.4187 0.698 0.0021 0.2874 3-methyl-2- 21047 61 0.0164 0.1281 0.1039 0.3961 0.5063 0.8289 oxobutyric acid Metabolite-4096 16186 61 0.0169 0.1281 0.1525 0.4771 0.3895 0.8005 Metabolite-9727 24077 50 0.0185 0.1342 0.0298 0.2368 0.9767 0.8893 Metabolite-1911 7933 61 0.0189 0.1342 0.6182 0.7691 0.0064 0.2874

Table 13 lists biomarkers for females, including, for each biomarker, the p-value and the q-value for the specified comparisons of groups as described above.

TABLE 13 Biomarkers from female subjects with Steatosis compared to Control subjects, steatohepatitis (NASH) compared to Control subjects and Steatosis compared to steatohepatitis (NASH) subjects. Control vs. NASH vs. Control vs. NASH STEATOSIS STEATOSIS COMPOUND COMP_ID LIB_ID p-value q-value p-value q-value p-value q-value glutamate 57 50 1.73E−08 3.22E−06 0.0023 0.0904 0.2067 0.6864 urate 1604 50 3.32E−07 3.08E−05 0.0278 0.2958 0.1005 0.6474 glutamate 12751 50 8.91E−07 1.00E−04 0.0036 0.0977 0.4745 0.6864 Metabolite-5769 18706 61 4.24E−06 2.00E−04 8.00E−04 0.0661 0.9332 0.7199 isocitrate 1113 61 8.14E−06 3.00E−04 0.255 0.4932 0.0342 0.4905 Metabolite-10026 24285 61 1.94E−05 6.00E−04 0.0231 0.2804 0.4083 0.6864 glycocholate 18476 61 2.91E−05 8.00E−04 0.0172 0.2557 0.5271 0.6864 L-kynurenine 15140 61 1.00E−04 0.0013 0.9893 0.73 0.0051 0.432 Metabolite-3330 13600 61 1.00E−04 0.0016 0.1335 0.3923 0.1878 0.6864 leucine 60 50 1.00E−04 0.0016 0.104 0.3726 0.241 0.6864 valine 1649 50 1.00E−04 0.0019 0.0938 0.3726 0.288 0.6864 isoleucine 1125 50 1.00E−04 0.0013 0.0574 0.3465 0.3262 0.6864 Metabolite-1110 5687 61 1.00E−04 0.0016 0.0032 0.0977 0.8124 0.6937 Metabolite-10951 31595 61 2.00E−04 0.0026 0.2368 0.4917 0.1503 0.6474 Metabolite-4522 16865 50 2.00E−04 0.0021 0.0689 0.359 0.3877 0.6864 Metabolite-4274 16511 50 2.00E−04 0.0021 0.0431 0.3397 0.516 0.6864 Isobar-47- 18882 61 2.00E−04 0.0021 0.032 0.3148 0.5941 0.6868 includes- taurochenodeoxycholic acid-and- taurodeoxycholic acid tyrosine 1299 61 3.00E−04 0.0026 0.0548 0.3465 0.492 0.6864 alanine 1126 50 4.00E−04 0.0036 0.1507 0.414 0.2717 0.6864 thyroxine 2761 61 5.00E−04 0.0043 0.2645 0.5014 0.1712 0.6644 Metabolite-2347 10551 61 5.00E−04 0.0048 0.0022 0.0904 0.4853 0.6864 Metabolite-10812 30821 50 7.00E−04 0.0058 0.1583 0.421 0.3247 0.6864 Metabolite-2269- 10245 61 7.00E−04 0.0056 0.0025 0.0904 0.4809 0.6864 glutamine 1647 50 9.00E−04 0.0067 0.1091 0.3726 0.458 0.6864 meso-erythritol 20699 50 0.001 0.0067 0.1995 0.463 0.2975 0.6864 Metabolite-3183 13214 61 0.001 0.0067 0.106 0.3726 0.4805 0.6864 docosahexaenoate- 19323 50 0.001 0.0067 0.0405 0.3397 0.7884 0.6896 DHA- Metabolite-4627 17068 61 0.001 0.0067 0.0268 0.2958 0.9307 0.7199 Isobar-66- 22803 61 0.0011 0.0067 0.0519 0.3465 0.7198 0.6896 includes- glycochenodeoxycholic acid- glycodeoxycholic acid 5-oxoproline 1494 50 0.0012 0.0076 0.1022 0.3726 0.5195 0.6864 Metabolite-10589 27801 61 0.0013 0.0078 0.2598 0.4988 0.2575 0.6864 mannose 584 50 0.0013 0.0078 0.0435 0.3397 0.8177 0.6937 Metabolite-4360 16650 50 0.0015 0.0083 0.6433 0.6655 0.0796 0.5826 gamma-L- 2734 61 0.0016 0.009 0.3615 0.5823 0.1942 0.6864 glutamyl-L- tyrosine Metabolite-1086 5628 61 0.0017 0.0092 0.6934 0.6655 0.0743 0.5826 phenylalanine 64 61 0.0019 0.0095 0.1746 0.4408 0.4085 0.6864 lactate 527 50 0.0019 0.0095 0.1018 0.3726 0.586 0.6868 arachidonate 1110 50 0.002 0.0098 0.8148 0.6934 0.0194 0.4834 Metabolite-7187 20267 61 0.0021 0.0098 0.747 0.6811 0.0705 0.5824 Metabolite-1914 7941 61 0.0021 0.0098 0.0075 0.1505 0.5693 0.6868 Metabolite-3109 12791 50 0.0024 0.0109 0.783 0.6934 0.0195 0.4834 tyrosine 12780 50 0.0031 0.0138 0.0321 0.3148 0.9228 0.7184 xanthine 3147 61 0.0032 0.0138 0.2695 0.5014 0.3371 0.6864 Metabolite-1496 6847 61 0.0035 0.0149 0.3752 0.5823 0.005 0.432 Metabolite-2041 8509 61 0.0037 0.0154 0.3041 0.5383 0.3177 0.6864 3-4-5- 18291 61 0.0045 0.0183 0.0182 0.2557 0.6793 0.6896 trimethoxy- cinnamic acid Metabolite-3951 15529 61 0.0049 0.0192 0.0586 0.3465 0.9515 0.7253 Metabolite-3994 16016 61 0.005 0.0192 0.0249 0.2887 0.7558 0.6896 Metabolite-3094 12774 50 0.0055 0.0209 0.6697 0.6655 0.0218 0.4834 Metabolite-9855 24233 61 0.0058 0.0216 0.124 0.3816 0.7024 0.6896 Metabolite-5726 18657 61 0.006 0.022 0.7399 0.6781 0.1182 0.6474 glycerol 15122 50 0.0065 0.0229 0.0928 0.3726 0.8347 0.6975 Metabolite-2395 10715 61 0.0066 0.0229 0.0016 0.0904 0.1741 0.6654 Metabolite-4986 17627 50 0.0067 0.0229 0.1963 0.4608 0.5502 0.6868 3-methyl-2- 21047 61 0.0069 0.0234 0.0928 0.3726 0.8462 0.6991 oxobutyric acid Metabolite-2259 10156 61 0.0071 0.0235 0.6427 0.6655 0.1628 0.6474 Metabolite-3230 13296 61 0.0072 0.0235 0.6656 0.6655 0.1545 0.6474 palmitate 1336 50 0.0075 0.0237 0.3757 0.5823 0.3312 0.6864 Metabolite-1911 7933 61 0.0075 0.0237 0.1736 0.4408 0.6183 0.6868 Metabolite-2291 10414 61 0.0079 0.0245 0.0098 0.1787 0.4316 0.6864 pyruvate 599 61 0.0118 0.0359 0.9667 0.7259 0.0792 0.5826 Isobar-1- 10737 61 0.0126 0.0375 0.055 0.3465 0.8389 0.6975 includes- mannose- fructose-glucose- galactose-alpha- L- sorbopyranose- Inositol-D- allose-D--altrose- D-psicone-L-- gulose-allo- inositol Metabolite-2272 10286 61 0.0128 0.0375 0.0731 0.359 0.94 0.7208 lysine 1301 50 0.0132 0.0384 0.271 0.5014 0.5375 0.6864 uridine 606 61 0.0139 0.0397 0.1016 0.3726 0.9509 0.7253 Metabolite-2390 10672 61 0.0143 0.0402 0.0557 0.3465 0.8179 0.6937 Metabolite-9043 22600 50 0.0154 0.0427 0.0118 0.2006 0.375 0.6864 Metabolite-2329 10544 61 0.0157 0.0429 0.3917 0.5823 0.4157 0.6864 Metabolite-9033 22570 50 0.0163 0.0437 0.0719 0.359 0.883 0.7052 Metabolite-4251 16496 50 0.018 0.0478 0.7335 0.6781 0.1998 0.6864 Metabolite-4276 16518 50 0.0183 0.0479 0.3795 0.5823 0.4523 0.6864 Metabolite-10932 31510 61 0.02 0.0515 0.6694 0.6655 0.2414 0.6864 Metabolite-3323 13557 61 0.0221 0.0561 0.4812 0.657 0.3823 0.6864 Metabolite-3016 12644 50 0.0229 0.0575 0.0038 0.0977 0.1636 0.6474 Metabolite-1981 8210 61 0.0248 0.0613 0.2371 0.4917 0.7138 0.6896 Metabolite-3088 12768 50 0.0272 0.0663 0.8115 0.6934 0.0842 0.5956 Metabolite-3087 12767 50 0.0295 0.0712 0.2356 0.4917 0.7535 0.6896 Metabolite-10797 30728 61 0.0309 0.0735 0.7815 0.6934 0.2311 0.6864 Metabolite-3100 12782 50 0.0316 0.0742 0.0624 0.3465 0.6928 0.6896 2-amino butyrate 12645 50 0.033 0.0756 0.3666 0.5823 0.5717 0.6868 threonine 1284 50 0.033 0.0756 0.1048 0.3726 0.8734 0.7052 Metabolite-3707 14837 61 0.0338 0.0761 0.019 0.2557 0.3591 0.6864 Metabolite-1834 7650 61 0.0343 0.0761 4.00E−04 0.0646 0.0289 0.4905 Metabolite-10785 30633 61 0.0344 0.0761 0.0612 0.3465 0.6678 0.6896 Metabolite-2139 9130 61 0.0349 0.0762 0.4999 0.6647 0.4382 0.6864 Metabolite-4613 17033 61 0.0375 0.0808 0.4934 0.663 0.4563 0.6864 gluconate 587 50 0.039 0.083 0.9105 0.7087 0.1966 0.6864 N-acetylglycine 27710 50 0.0396 0.083 0.1303 0.3923 0.9192 0.7177 Metabolite-10507 27275 50 0.0398 0.083 0.8432 0.6996 0.1135 0.6474 threonate 27738 50 0.0404 0.0834 0.5847 0.6655 0.3906 0.6864 p- 1431 50 0.0411 0.0837 0.7669 0.6934 0.0954 0.6428 hydroxyphenyl- lactate-HPLA- palmitoleate 1507 50 0.0427 0.0854 0.6825 0.6655 0.3285 0.6864 Metabolite-3995 16019 61 0.0428 0.0854 0.6036 0.6655 0.3849 0.6864 Metabolite-2231 9905 61 0.0463 0.0913 0.3818 0.5823 0.6207 0.6868 Metabolite-3165 13142 61 0.0481 0.0939 0.0439 0.3397 0.4996 0.6864 stearate 1358 50 0.0486 0.0939 0.7225 0.6781 0.3214 0.6864 Metabolite-9726 24076 50 0.0504 0.0954 0.6042 0.6655 0.0688 0.5824 serine 12663 50 0.0504 0.0954 0.578 0.6655 0.4331 0.6864 Metabolite-5774 18723 61 0.0514 0.0964 0.8537 0.6996 0.254 0.6864 Metabolite-10933 31518 50 0.0578 0.1062 0.2685 0.5014 0.0197 0.4834 Metabolite-2321 10501 61 0.0918 0.1468 0.3393 0.5814 0.0402 0.4905 Isobar-60- 22261 61 0.1198 0.1696 0.2887 0.5228 0.0389 0.4905 includes-s-2- hydroxybutyric acid-2- hydroxyiso- butyric acid threonine 12666 50 0.131 0.18 0.3057 0.5383 0.0454 0.5204 Metabolite-2821 11923 61 0.1533 0.1987 5.00E−04 0.0646 0.0098 0.4834 creatine 27718 61 0.1942 0.2211 0.1218 0.3816 0.0183 0.4834 paraxanthine 18254 61 0.2253 0.247 0.0823 0.3726 0.013 0.4834 Metabolite-3443 14125 61 0.2434 0.258 0.1778 0.4408 0.0371 0.4905 fructose 577 50 0.2708 0.2732 0.2032 0.4674 0.0487 0.5204 theophylline 18394 61 0.3262 0.301 0.15 0.414 0.0399 0.4905 alpha-keto- 528 61 0.364 0.3232 0.0187 0.2557 0.004 0.432 glutarate Metabolite-6346 19402 50 0.4153 0.3451 0.0919 0.3726 0.029 0.4905 Metabolite-8506 21762 61 0.4328 0.3491 0.0711 0.359 0.0227 0.4834 Metabolite-1667 7132 61 0.4557 0.3628 0.1102 0.3726 0.0398 0.4905 caffeine 569 61 0.462 0.3658 0.036 0.3397 0.0114 0.4834 Metabolite-4238 16471 61 0.9278 0.51 0.0226 0.2804 0.0282 0.4905

Example 5 Analytical Characteristics Analytical Characterization of Unnamed Biomarkers Compounds:

Table 14 below includes analytical characteristics of each of the isobars and the unnamed metabolites listed in Tables 1-3 and 8-13 above. The table includes, for each listed Metabolite, the Metabolite ID (ID), the retention time (RT), retention index (RI), mass, library, and polarity obtained using the analytical methods described above. “Metabolite ID” refers to the unique identifier for that compound in our internal chemical library database. “Mass” refers to the mass of the C12 isotope of the parent ion used in quantification of the compound. The values for “Library” give an indication of the analytical method used for quantification: “50” indicates GC-MS and “61”, “200” and “201” indicate LC-MS. “Polarity” indicates the polarity of the quantitative ion as being either positive (+) or negative (−).

TABLE 14 Analytical Characteristics of Unnamed Metabolites. ID Biomarker Library RT RI MASS Polarity 1882 Isobar 47 includes 61 15.51 15700.0 498.4 − taurochenodeoxycholic acid and taurodeoxycholic acid 5628 Metabolite - 1086 61 4.56 4811.0 294.1 + 6398 Metabolite - 1335 61 8.74 9162.2 367.2 + 6847 Metabolite - 1496 61 1.53 1562.0 133.0 − 7933 Metabolite - 1911 61 11.42 11799.6 464.1 + 10501 Metabolite - 2321 61 13.44 12940.0 314.3 + 10629 Metabolite - 2386 61 11.94 12150.0 539.2 − 10737 Isobar 1 includes mannose, 61 1.45 1481.0 215.0 − fructose, glucose, galactose, alpha-L-sorbopyranose, Inositol, D-allose,D-(+)- altrose,D-psicone,L-(+)-gulose, allo-nositol 11923 Metabolite - 2821 61 6.80 7980.0 119.1 + 12593 Metabolite-2973 50 4.74 1213.4 281 + 12604 Metabolite-2981 50 5.21 1265.2 210.9 + 12626 Metabolite-3003 50 6.79 1446.6 218.1 + 12754 Metabolite-3075 50 10.36 1857.9 204 + 12757 Metabolite-3078 50 10.65 1887 203.1 + 12761 Metabolite-3081 50 10.89 1911.5 204 + 12763 Metabolite-3083 50 10.94 1916.1 204 + 12769 Metabolite-3089 50 11.28 1951.5 116.9 + 12770 Metabolite-3090 50 11.31 1955 243.1 + 12771 Metabolite-3091 50 11.41 1966.2 232.1 + 12774 Metabolite-3094 50 11.55 1980.6 299 + 12781 Metabolite-3099 50 11.77 2005.2 204 + 12782 Metabolite-3100 50 11.85 2013.2 204 + 12783 Metabolite-3101 50 11.93 2022.2 290 + 12785 Metabolite-3103 50 12.09 2039.8 290.1 + 12789 Metabolite-3107 50 12.21 2053.2 204.1 + 12790 Metabolite-3108 50 12.24 2056.5 246 + 13142 Metabolite - 3165 61 8.38 8472.2 265.0 + 13214 Metabolite - 3183 61 9.37 9441.0 295.2 + 13296 Metabolite - 3230 61 3.10 3043.2 245.0 + 13557 Metabolite - 3323 61 14.47 14696.5 624.4 − 13600 Metabolite - 3330 61 14.06 14292.2 514.3 − 15529 Metabolite - 3951 61 8.41 8705.4 367.1 + 16016 Metabolite - 3994 61 1.63 1640.4 427.0 + 16019 Metabolite - 3995 61 2.19 2230.0 207.7 − 16138 Metabolite-4080 50 14.02 2270.2 299 + 16308 Metabolite-4147 50 10.07 1767.1 290.2 + 16337 Metabolite - 4167 61 11.03 10920.4 286.2 + 16650 Metabolite-4360 50 9.15 1678.2 347.2 + 16665 Metabolite-4364 50 10.66 1852.4 232 + 16666 Metabolite-4365 50 11.05 1892.9 204 + 16819 Metabolite-4496 50 6.76 1398.2 204 + 16829 Metabolite-4503 50 8.39 1589 227.2 + 16865 Metabolite-4522 50 12.26 2025.4 217.1 + 16959 Metabolite-4595 50 5.65 1274.4 130 + 17028 Metabolite-4611 50 8.07 1546.6 292.1 + 17033 Metabolite - 4613 61 12.73 12778.0 541.4 − 17068 Metabolite - 4627 61 10.84 11034.9 591.3 + 17304 Metabolite - 4759 61 11.80 11906.0 310.2 + 17627 Metabolite-4986 50 11.56 1956.4 204.1 + 18232 Metabolite-5403 50 5.92 1300.2 319 + 18591 Metabolite - 5646 61 8.96 8750.0 228.0 − 18657 Metabolite - 5726 61 14.05 13757.0 417.3 − 18706 Metabolite - 5769 61 11.12 10753.2 485.2 − 18868 Metabolite-5847 50 12.35 2040 288.2 + 18929 Metabolite-5907 50 8.69 1643.2 229.1 + 19363 Metabolite-6227 50 5 1210.5 196.1 + 19368 Metabolite-6267 50 9.32 1704.5 257.1 + 19370 Metabolite-6268 50 9.91 1773.8 271.1 + 19374 Metabolite-6270 50 11.35 1929.6 320.2 + 19402 Metabolite-6346 50 8 1550.8 263.2 + 19490 Metabolite-6488 50 12.25 2021.7 204.1 + 19985 Metabolite-6957 50 11.71 1966 290.1 + 20267 Metabolite - 7187 61 8.38 8500.0 328.0 + 20950 Metabolite-7846 50 5.1 1208.1 145.1 + 21418 Isobar 56 - pipecolate; 61 2.45 2850.0 130.1 + aminocyclopentane carboxylic acid 21762 Metabolite - 8506 61 8.92 8811.0 475.1 + 22261 Isobar 60 includes (s)-2- 61 4.26 4725.0 148.9 − hydroxybutyric acid,2- hydroxyisobutyric acid 24233 Metabolite - 9855 61 11.85 11605.3 462.2 − 24285 Metabolite - 10026 61 9.47 9300.3 259.1 − 27801 Metabolite - 10589 61 15.43 17536.8 619.3 − 30633 Metabolite - 10785 61 11.49 12073.4 363.0 + 30728 Metabolite - 10797 61 10.92 11563.3 352.2 − 31510 Metabolite - 10932 61 11.54 11928.0 190.0 + 31529 Metabolite - 10941 61 3.60 4101.0 191.8 + 32514 Metabolite-11200 200 5.62 5637 496.4 + 32516 Metabolite-11202 200 5.8 5823 524.4 + 32517 1-oleoylglycerophosphocholine 200 5.65 5665 522.4 + (Metabolite-11203) 32518 Metabolite-11204 200 5.26 5263 229.2 + 32519 1-linoleoylglycerophosphocholine 200 5.55 5558 520.4 + (Metabolite-11205) 32520 Metabolite-11206 200 0.59 575 138.8 + 32545 Metabolite-11228 201 1.15 1136 278.1 − 32547 Metabolite-11230 201 1.44 1438 278.1 − 32548 Metabolite-11231 201 1.47 1471 330 − 32549 Metabolite-02269 201 1.55 1551 255.1 − 32550 Metabolite-02272 201 1.97 1958 189 − 32551 Metabolite-11234 201 2.01 2001 334.1 − 32552 Metabolite-11235 201 2.04 2033 326.1 − 32553 Metabolite-03832 201 2.2 2199 173.1 − 32557 Metabolite-06126 201 2.69 2684 203.1 − 32559 Metabolite-11242 201 2.96 2958 360 − 32560 Metabolite-07765 201 3.71 3705 245.1 − 32561 Metabolite-11244 201 3.78 3771 224.2 − 32562 Metabolite-11245 201 3.91 3902 238.3 − 32563 Metabolite-11246 201 3.94 3930 143.2 − 32564 Metabolite-11247 201 3.94 3932 213.1 − 32566 Metabolite-11249 200 1.24 1271 280.1 + 32567 Metabolite-11250 200 1.3 1339 150.2 + 32571 Metabolite-11254 200 2.05 2095 328.1 + 32572 Metabolite-11255 200 2.38 2442 247.1 + 32575 Metabolite-11258 200 2.89 2941 362 + 32578 Metabolite-11261 200 3.69 3732 286.2 + 32587 Metabolite-02249 201 4.03 4025 267.2 − 32588 Metabolite-01327 201 4.25 4242 583.2 − 32590 Metabolite-11273 201 4.56 4552 369.2 − 32593 Metabolite-02036 200 4.94 4985 616.2 + 32596 Metabolite-02250 200 5.14 5158 286.2 + 32599 Metabolite-11282 201 4.77 4763 254.8 − 32602 Metabolite-11285 200 5.28 5260 288.1 + 32609 Metabolite-01345 201 4.9 4887 369.2 − 32616 Metabolite-11299 201 4.9 4893 507.2 − 32619 Metabolite-11302 201 5.01 4998 397.3 − 32620 Metabolite-11303 201 5.02 5015 512.3 − 32621 Metabolite-11304 200 0.8 801 217.1 + 32625 Metabolite-11308 201 5.15 5133 365.3 − 32631 Metabolite-11314 200 0.64 634 243 + 32632 Metabolite-11315 200 1.19 1210 130.2 + 32634 Metabolite-11317 201 5.81 5703 219.3 − 32635 Metabolite-11318 201 5.81 5699 476.3 − 32636 Metabolite-11319 201 5.81 5700 269.4 − 32637 Metabolite-11320 201 5.85 5740 593.9 − 32638 Metabolite-11321 201 6.15 5997 569.9 − 32641 Metabolite-11324 201 6.24 6075 596 − 32643 Metabolite-11326 201 6.35 6160 213.2 − 32644 Metabolite-11327 200 5.16 5176 269.2 + 32648 Metabolite-11331 201 0.69 686 164.2 − 32651 Metabolite-11334 200 0.96 982 259.1 + 32652 Metabolite-11335 200 0.97 991 229.2 + 32654 Metabolite-11337 200 1 1020 160.2 + 32656 Metabolite-11339 201 0.69 689 156.2 − 32670 Metabolite-11353 200 0.8 811 203.2 + 32671 Metabolite-11354 200 0.76 770 146.2 + 32672 Metabolite-02546 200 0.75 764 129.2 + 32675 Metabolite-03951 200 1.87 1912 367.1 + 32682 Metabolite-11365 201 5.61 5527 303.3 − 32689 Metabolite-11372 201 5.35 5303 467.4 − 32691 Metabolite-11374 200 0.69 690 370.9 + 32692 Metabolite-11375 200 0.73 732 309.1 + 32694 Metabolite-11377 200 0.77 770 249.2 + 32696 Metabolite-11379 201 5.65 5566 267.3 − 32697 Metabolite-11380 200 1.04 1053 339.8 + 32698 Metabolite-11381 200 1.11 1126 186.2 + 32699 Metabolite-11382 200 1.16 1180 250.8 + 32702 Metabolite-11385 200 1.38 1430 312.1 + 32703 Metabolite-11386 200 1.47 1525 344.1 + 32704 Metabolite-11387 200 1.65 1699 294.1 + 32707 Metabolite-11390 200 2 2041 100.2 + 32709 Metabolite-03056 200 2.21 2264 185.2 + 32710 Metabolite-11393 200 2.34 2403 367.1 + 32717 Metabolite-11400 200 2.81 2855 710.1 + 32718 Metabolite-01342 200 2.8 2848 265.1 + 32722 Metabolite-11405 200 3.02 3075 264.8 + 32729 Metabolite-11412 200 3.78 3836 204.2 + 32732 Metabolite-11415 201 0.69 692 313.1 − 32734 Metabolite-10954 200 4.14 4229 288.2 + 32735 Metabolite-01911 200 4.26 4354 464.1 + 32738 Metabolite-11421 200 4.54 4634 314.2 + 32739 Metabolite-11422 201 0.89 884 151.1 − 32740 Metabolite-11423 201 1.05 1038 260.1 − 32741 Metabolite-11424 200 4.58 4664 227.1 + 32744 Metabolite-11427 201 1.16 1145 181.1 − 32746 Metabolite-11429 201 1.16 1151 245.1 − 32747 Metabolite-01142 201 1.19 1176 117.2 − 32748 Metabolite-11431 201 1.58 1575 330 − 32749 Metabolite-11432 201 1.89 1886 292.1 − 32751 Metabolite-11434 201 2.11 2107 370.1 − 32752 Metabolite-11435 201 2.14 2131 365.1 − 32753 Metabolite-09789 201 2.62 2613 153.1 − 32754 Metabolite-11437 201 2.89 2888 231 − 32755 Metabolite-11438 201 3.1 3092 241.2 − 32756 Metabolite-02276 201 3.35 3339 199.1 − 32757 Metabolite-11440 201 3.58 3571 246.3 − 32758 Metabolite-11441 201 3.78 3773 331.1 − 32759 Metabolite-11442 201 3.91 3902 331.1 − 32760 Metabolite-11443 201 3.92 3910 225.3 − 32761 Metabolite-11444 201 3.99 3983 541.2 − 32762 Metabolite-11445 201 4.01 3995 239.3 − 32764 Metabolite-11447 200 0.98 989 278.1 + 32767 Metabolite-11450 201 4.11 4103 224.2 − 32769 Metabolite-11452 201 4.12 4109 352.1 − 32776 2-methylbutyrylcarnitine 200 2.49 2545 246.2 + (Metabolite-11459) 32786 Metabolite-11469 200 3.82 3874 239.1 + 32787 Metabolite-11470 201 4.16 4151 525.2 − 32792 Metabolite-11475 201 4.25 4240 383.2 − 32793 Metabolite-11476 200 4.52 4616 189.1 + 32795 Metabolite-11478 201 4.3 4286 165.2 − 32797 Metabolite-11480 201 4.42 4406 229 − 32800 Metabolite-11483 201 4.45 4443 505.2 − 32802 Metabolite-11485 201 4.49 4478 378.2 − 32807 Metabolite-11490 201 4.77 4762 279.8 − 32808 Metabolite-11491 201 4.85 4846 567.3 − 32813 Metabolite-11496 201 5.58 5508 271.3 − 32814 Metabolite-11497 201 5.37 5324 233.3 − 32815 Metabolite-11498 201 5.78 5674 500.3 − 32822 Metabolite-11505 201 1.61 1614 101.2 − 32825 Metabolite-11508 201 2.97 2987 173.2 − 32827 Metabolite-11510 201 3.92 3925 385.2 − 32829 Metabolite-03653 200 0.82 826 144.2 + 32838 Metabolite-11521 200 3.71 3755 286.2 + 32839 Metabolite-11522 201 4.76 4754 313.2 − 32845 Metabolite-11528 200 4.74 4832 316.3 + 32846 Metabolite-11529 201 4.85 4845 624.3 − 32847 Metabolite-11530 201 4.87 4866 313.2 − 32848 Metabolite-11531 201 4.86 4850 391.3 − 32854 Metabolite-11537 200 5.14 5160 366.3 + 32855 Metabolite-11538 201 4.93 4920 311.3 − 32858 Metabolite-11541 201 4.99 4984 399 − 32863 Metabolite-11546 201 5.02 5015 448.4 − 32865 Metabolite-11548 201 5.09 5081 391.3 − 32866 Metabolite-11549 201 5.11 5093 339.3 − 32878 Metabolite-11561 201 1.26 1252 267.1 − 32879 Metabolite-11562 201 0.98 972 197.1 − 32880 Metabolite-11563 201 0.98 971 115.2 − 32881 Metabolite-11564 201 1.2 1188 177.1 − 32910 Metabolite-11593 201 0.79 790 189.2 − 32912 Metabolite-11595 201 0.92 919 665 − 32914 Metabolite-11597 201 1 991 665 − 32926 Metabolite-11609 201 1.48 1490 310.1 − 32945 Metabolite-11628 201 3.14 3133 360.1 − 32952 Metabolite-02277 201 3.61 3604 201.1 − 32954 Metabolite-06132 201 3.66 3655 321.1 − 32965 Metabolite-11648 201 4.84 4819 534.3 − 32970 Metabolite-11653 201 5.82 5686 331.3 − 32971 Metabolite-11654 200 2.53 2500 246.2 + 32978 Metabolite-11656 200 0.6 612 227 + 33012 Metabolite-11674 200 1.14 1151 189.1 + 33072 Metabolite-11727 200 0.71 709 241 + 33073 cysteine-glutathione disulfide 200 0.8 804 427 + (Metabolite-11728) 33084 Metabolite-11739 200 3.15 3239 769 + 33131 Metabolite-11786 200 0.87 864 136 + 33132 Metabolite-11787 200 1.13 1126 148.1 + 33133 Metabolite-11788 200 1.17 1161 245 + 33138 Metabolite-11793 200 3.57 3634 601.1 + 33140 Metabolite-11795 200 1.46 1457 148.1 + 33154 Metabolite-11809 200 5.23 5248 381.5 + 33159 Metabolite-11814 200 2.39 2372 152.1 + 33163 Metabolite-11818 200 2.56 2535 146.1 + 33169 Metabolite-11824 201 1.4 1402 326.1 − 33171 Metabolite-11826 201 1.48 1489 194.1 − 33173 Metabolite-11828 201 1.69 1703 246.1 − 33174 Metabolite-11829 201 1.8 1813 230 − 33178 Metabolite-11833 201 1.97 1977 260.1 − 33183 Metabolite-11838 201 2.3 2314 276 − 33188 Metabolite-11843 201 2.69 2710 230.1 − 33190 Metabolite-11845 201 2.87 2891 615 − 33192 Metabolite-11847 201 3.05 3074 259.2 − 33194 Metabolite-11849 201 3.2 3229 266.2 − 33198 Metabolite-11853 201 3.59 3602 187.1 − 33203 Metabolite-11858 201 4.42 4400 437.1 − 33206 Metabolite-11861 201 4.63 4617 229.2 − 33209 Metabolite-11864 201 5.02 5012 280.9 − 33216 Metabolite-11871 200 5.25 5249 370.3 + 33219 Metabolite-11874 201 5.23 5199 197.3 − 33225 Metabolite-11880 201 5.44 5378 537.4 − 33227 Metabolite-11882 201 5.52 5445 301.3 − 33228 1-arachidonoylglycerophospho- 200 5.54 5524 544.3 + choline (Metabolite-11883) 33232 Metabolite-11887 201 5.85 5736 307.4 − 33237 Metabolite-11892 201 0.71 710 367.1 − 33242 Metabolite-11897 201 1.69 1664 292.1 − 33248 Metabolite-11903 201 2.53 2484 283.1 − 33250 Metabolite-11905 201 4.44 4401 283.2 − 33252 Metabolite-11907 201 5.06 5036 313.3 − 33254 Metabolite-11909 201 5.3 5272 297.3 − 33258 Metabolite-11913 201 5.43 5402 275.3 − 33265 Metabolite-11920 200 0.68 686 268.1 + 33323 Metabolite-11977 200 3.21 3287 270.1 + 33366 Metabolite-12020 200 5.15 5178 424.4 + 33380 Metabolite-12029 201 0.68 683 329.1 −

Example 6

Additional metabolomic analysis was performed on the samples obtained from the study cohorts described in Example 1. Additional statistical analysis was also performed as described below.

Metabolomic Analysis

The global, unbiased metabolic profiling platform was based on a combination of 3 independent platforms: ultra-high-performance liquid chromatography/tandem mass spectrometry (UHPLC/MS/MS) optimized for basic species, UHPLC/MS/MS optimized for acidic species, and gas chromatography/mass spectrometry (GC/MS). The major components of the process are summarized as follows:

Sample Extraction:

One hundred microliters of each plasma sample was thawed on ice and extracted using an automated MicroLab STAR system (Hamilton, Salt Lake City, Utah) in 400 μL of methanol containing the recovery standards.

GC MS and UHPLC/MS MS Analysis:

UHPLC/MS was carried out using a Waters Acquity UHPLC (Waters, Milford, Mass.) coupled to an LTQ mass spectrometer (Thermo Fisher Scientific, Waltham, Mass.) equipped with an electrospray ionization source. Two separate UHPLC/MS injections were performed on each sample: one optimized for positive ions and one for negative ions. Derivatized samples for GC/MS were analyzed on a Thermo-Finnigan Trace DSQ fast-scanning single-quadrupole MS operated at unit mass resolving power. Chromatographic separation followed by full-scan mass spectra was carried out to record retention time, molecular weight (m/z), and MS/MS of all detectable ions presented in the samples.

Metabolite Identification:

Metabolites were identified by automated comparison of the ion features in the experimental samples to a reference library of chemical standard entries that included retention time, molecular weight (m/z), preferred adducts, and in-source fragments as well as their associated MS/MS spectra. This library allowed the rapid identification of metabolites in the experimental samples with high confidence.

Data Imputation and Statistical Analysis:

The samples were analyzed over the course of 2 days. After the data were corrected for minor variations resulting from instrument interday tuning differences, the missing values for a given metabolite were imputed with the observed minimum detection value on the assumption that they were below the limits of detection. For the convenience of data visualization, the raw area counts for each biochemical were rescaled by dividing each sample value by the median value for the specific biochemical. Statistical analysis of the data was performed using JMP (SAS, Cary, N.C.), a commercial software package, and “R”, which is a freely available open-source software package. A log transform was applied to the observed relative concentrations for each biochemical because, in general, the variance increased as a function of a biochemical's average response. Welch t tests were performed to compare data obtained from experimental groups. Multiple comparisons were accounted for with the false discovery rate method, and each false discovery rate was estimated using q values. Random forest (RF) analysis was performed on untransformed data using R. Random forest is a supervised classification technique based on an ensemble of decision trees. For a given decision tree, a subset of samples is selected to build the tree; and then the remaining samples are predicted from this tree. This process is repeated thousands of times to produce a forest. The final classification is determined by computing the frequencies (“votes”) of predictions for each group over the whole forest. This method is unbiased because the prediction for each sample is based on trees built from a subset of samples not including it; thus, the prediction accuracy is an unbiased estimate of predicting a new data set. To see which variables contribute the most to the separation, an “importance” measure is computed. We used the “mean decrease accuracy” as this metric. This value is determined by randomly permuting a variable and then running the values through the trees and reassessing the prediction accuracy. If a variable is not important, then this procedure will have little change in the accuracy (permuting random noise will give random noise), whereas if a variable is important, the accuracy will drop after such a permutation.

Metabolomic Analysis Results

Using LC/MS and GC/MS analysis, 437 distinct metabolites were identified in the plasma sample. Of these, 228 biochemicals matched a named structure in the reference library. The remaining 209 biochemicals represent distinct chemical entities, but they do not match a named biochemical in the reference library. The changes in the metabolites between the experimental groups were calculated by the ratio of their group means. The statistical significance of the changes was analyzed by Welch t test, with P<0.05 deemed to be significant. Some of the results are described below.

Bile Acids

As shown in FIG. 8, there was a 4-fold increase in the plasma concentration of glycocholate and taurocholate and a 2-fold increase in glycochenodeoxycholate in subjects with NASH as compared with controls. These bile acids were also higher in the steatosis group compared with controls; however, only taurocholate met the statistical significance cutoff of P<0.05.

Table 15 provides additional statistical data for the bile acids (from the bile acid metabolism subpathway). The P values for the listed biomarkers were all <0.1.

TABLE 15 Bile acid biomarkers % Change (NASH)/ (STEATOSIS)/ (STEATOSIS)/ Name Kegg HMDB (CONTROL) (CONTROL) (NASH) cholate C00695, HMDB00619 125% 42% −37% C04661, C01558 glycocholate C01921 HMDB00138 331% 205% −29% taurocholate C05122 303% 115% −47% taurochenodeoxy- C05465 HMDB00951 24% 85% 48% cholate taurodeoxycholate C05463 HMDB00896 115% 11% −48% glycodeoxycholate C05464 HMDB00631 247% 23% −65% glycochenodeoxy- C05466, HMDB00708, 103% 144% 21% cholate C05462 HMDB00637

Glutathione Metabolism

Consistent with decreased plasma glutathione in subjects with steatosis and NASH (Table 16), the concentration of cysteine-glutathione disulfide, a product of glutathione and cysteine conjugate, was significantly lower in subjects with steatosis and NASH (FIG. 9E). In addition, several glutamyl dipeptides-glutamyl valine, glutamyl leucine, glutamyl phenylalanine, and glutamyl tyrosine-were higher in both NASH and steatosis. The increase was of similar magnitude in both groups.

In Table 16, the rows represent the actual groups and the columns list the predicted groupings by metabolomic analysis.

TABLE 16 Confusion matrix of sample by RF analysis Healthy NASH Steatosis Class error Healthy 23 0 2 0.08 NASH 2 11 11 0.54 Steatosis 1 6 4 0.64

Lipids

Subtle differences in lipid profiles were found between subjects with NAFLD and the healthy controls. Several free fatty acids—eicosapentaenoate (C20:5n3), docosahexaenoate (C22:6n3), 10-undecenoate (C11:1n1), and arachidonate (C20:4n6)—were significantly lower in individuals with NASH as compared with controls. In contrast, only caprate (C10:0) and 10-undecenoate (C11:1n1) were significantly lower in subjects with steatosis as compared with controls. Only linolenate (C18:3n3 or 6) and undecanoate (C11:0) were significantly higher in subjects with steatosis when compared with those with NASH. There were no other differences in the fatty acids profile among steatosis and NASH subjects. Metabolomic analysis could quantify 14 species of camitine in the plasma. Free carnitine and butyrylcarnitine levels were significantly elevated in both steatosis and NASH compared with controls (FIG. 10A, E). In addition, propionylcamitine and 2-methylbutyrylcamitine levels were significantly higher in subjects with NASH only. Significant differences in the levels of lysophosphocholines were observed between individuals with NASH and controls. Specifically, the concentration of glycerophosphocholine, 1-oleoylglycerophosphocholine, 1-linoleoylglycerophosphocholine, and 1-arachidonoylglycerophosphocholine were significantly lower in NASH when compared with controls. Only 1-oleoylglycerophosphocholine was significantly lower in subjects with steatosis.

Carbohydrates

Glucose and pyruvate were significantly higher in subjects with NASH. Mannose and lactate levels were higher in both steatosis and NASH. In addition, erythronate levels were higher in subjects.

Amino Acids

Among the essential amino acids, phenylalanine, and branched-chain amino acids, leucine, isoleucine, and valine were higher in subjects with NASH as compared with controls (FIG. 11). Glutamate, aspartate, and tyrosine were also elevated in individuals with NASH. In contrast to subjects with NASH, only glutamate, lysine, tyrosine, and isoleucine were significantly higher in subjects with steatosis compared with controls. There were no significant differences in amino acid levels among subjects with steatosis and NASH.

Others

A number of unnamed biochemicals in the plasma were significantly higher in NAFLD subjects, in particular Metabolite-11546 and Metabolite-11529, which were almost 3-fold higher in NASH compared with controls.

NASH Vs. Steatosis

Plasma levels of very few metabolites were significantly different in subjects with steatosis and NASH. These include glutamate, creatine, pyruvate, and unknown Metabolite-01911_200, which were significantly lower, and undecenoate (C11:0) and linolenate (α or γ), which were significantly higher in subjects with steatosis when compared with those with NASH.

RF Analysis

An RF analysis of the plasma biochemical profile data was performed to test the ability of the metabolomic data to correctly classify the samples into their respective groups (Table 16). For the healthy group, 92% of the subjects (23 of 25) could be correctly separated from the individuals with NAFLD (steatosis and steatohepatitis), with a class error of 0.08. Among the NAFLD subjects, the error rate was high for subjects in the NASH and steatosis groups, suggesting that the metabolic profile of the NASH and steatosis group was not distinguishable based on this data set. The metabolites that most effectively separated the groups are shown in the importance plot (FIG. 12). Because the steatosis group was small (not powered enough), we also performed an RF analysis between subjects with NASH only and healthy controls. Twenty-three of 24 subjects with NASH could be separated from healthy controls, with an error rate of 4.1%. The metabolites that most effectively separated the group are shown in the importance plot (FIG. 13).

RF and Principal Component Analysis

To assess the ability to classify subjects as healthy, with steatosis, or with NASH, RF analysis was performed using the entirety of the metabolomic data. An excellent separation of the healthy subjects and NAFLD subjects was achieved. However, the steatosis and NASH subjects were not readily distinguishable (Table 16). This is consistent with the result from Welch t test. Many metabolites were deemed to be statistically significant when either the steatosis group or the NASH group was compared with the healthy control group. Only a few metabolites were significantly different between the steatosis and the NASH groups. It is worth noting that the number of subjects in the steatosis group was rather limited (n=11), and the statistical significance is impacted by the group size. As shown in FIG. 13, a panel of markers that provided the most contribution to the separation of the healthy group and NASH group was discovered. Not surprisingly, these markers matched with the metabolites identified by the Welch t tests (e.g., glutathione metabolites, bile acids, amino acids, etc.)

While the invention has been described in detail and with reference to specific embodiments thereof, it will be apparent to one skilled in the art that various changes and modifications can be made without departing from the spirit and scope of the invention. 

1-25. (canceled)
 26. A method of determining the presence, absence or level(s) of one or more metabolites in a sample from a subject, the method comprising a) extracting small molecules from a blood, blood serum, or blood plasma sample obtained from the subject to produce an analytical sample; and b) performing liquid chromatography-mass spectrometry on the analytical sample to determine a level of xanthine and one or more additional metabolites in the sample, wherein the one or more additional metabolites are selected from the group consisting of kynurenine, glutamyl valine, gamma glutamylphenylalanine, gamma glutamylleucine, 2-amino butyrate, 3-4-5-trimethoxy-cinnamic acid, 3-carboxyl-4-methyl-5-propyl-2-furanpropanoic acid, 3-methyl-2-oxobutyrate, alpha-ketoglutarate, bradykinin, erythritol, gamma tocopherol, gluconate, glycerate, histidine, isocitrate, lysine, mannose, N-acetylglycine, nicotinamide, phenylacetate, p-hydroxyphenyllactate, pyruvate, quinate, serine, threonate, threonine, thyroxine, trans-4-hydroxyproline, urate, uridine, urocanate, taurocholate, taurine, glycocholate, glycochenodeoxycholate, taurochenodeoxycholate and combinations thereof.
 27. A method of determining the presence, absence or level(s) of one or more metabolites in a sample from a subject, the method comprising a) extracting small molecules from a blood, blood serum, or blood plasma sample obtained from the subject to produce an analytical sample; b) performing liquid chromatography-mass spectrometry on the analytical sample to determine a level of xanthine and one or more additional metabolites in the sample, wherein the one or more additional metabolites are selected from the group consisting of kynurenine, glutamyl valine, gamma glutamylphenylalanine, gamma glutamylleucine, 2-amino butyrate, 3-4-5-trimethoxy-cinnamic acid, 3-carboxyl-4-methyl-5-propyl-2-furanpropanoic acid, 3-methyl-2-oxobutyrate, alpha-ketoglutarate, bradykinin, erythritol, gamma tocopherol, gluconate, glycerate, histidine, isocitrate, lysine, mannose, N-acetylglycine, nicotinamide, phenylacetate, p-hydroxyphenyllactate, pyruvate, quinate, serine, threonate, threonine, thyroxine, trans-4-hydroxyproline, urate, uridine, urocanate, taurocholate, taurine, glycocholate, glycochenodeoxycholate, taurochenodeoxycholate and combinations thereof; and c) classifying the subject as either having or not having non-alcoholic steatohepatitis with at least 80% accuracy based on the level of xanthine and the one or more additional metabolites.
 28. A method of determining the presence, absence or level(s) of one or more metabolites in a sample from a subject, the method comprising a) extracting small molecules from a blood, blood serum or blood plasma sample obtained from the subject to produce an analytical sample; and b) performing liquid chromatography-mass spectrometry on the analytical sample to determine a level of cysteine-glutathione disulfide and one or more additional metabolites in the sample, wherein the one or more additional metabolites are selected from the group consisting of taurocholate, taurine, glycocholate, glycochenodeoxycholate, taurochenodeoxycholate and combinations thereof.
 29. The method of claim 28, further comprising classifying the subject as either having or not having non-alcoholic steatohepatitis with at least 80% accuracy based on the level of cysteine-glutathione disulfide and the one or more additional metabolites. 